Patent Description:
The present invention relates generally to the monitoring of mechanical machines and more particularly to the identification of faults of mechanical machines by the monitoring thereof.

Various systems and methods for fault identification of mechanical machines are known in the art.

<CIT> discloses systems, apparatus, processes, and methods for generating and deploying dynamically updated predictive models. The predictive model may be deployed for the purpose of predicting operational outcomes of interests in operating systems, hardware devices, machines and/or processes associated therewith prior to the operational outcomes of interest occurring. The predictions can, for example, provide sufficient time for maintenance or repairs to be scheduled and carried out to avoid the predicted operational outcome.

<CIT> discloses a self-improving classifier which receives sensor data of operation of a machine. Categories are defined, each associated with one or more parameters. It is determined whether the sensor data matches one or more parameters of a first category and responsive to a determination that the sensor data matches the one or more parameters of the first category, the approach classifies the sensor data into the first category.

The present invention seeks to provide novel systems and methods for the use of transfer learning between different types of sensors monitoring signals emanating from mechanical machines, for the purpose of fault identification and maintenance of the monitored mechanical machines.

According to a first aspect of the present invention there is provided a method for identifying a fault of at least one mechanical machine, including causing a first plurality of sensors coupled to a corresponding first plurality of mechanical machines to acquire a first plurality of sets of signals emanating from the first plurality of mechanical machines, the first plurality of mechanical machines sharing at least one characteristic, supplying at least the first plurality of sets of signals of the first plurality of mechanical machines to a pre-existing fault classifier comprising a neural network previously trained to automatically identify faults of a second plurality of mechanical machines based on signals emanating therefrom and previously acquired by a second plurality of sensors, the second plurality of sensors being of a different type than the first plurality of sensors, the second plurality of mechanical machines sharing the at least one characteristic, modifying the pre-existing fault classifier by employing transfer learning, based at least on the first plurality of sets of signals of the first plurality of mechanical machines, thereby providing a modified fault classifier, said modifying comprising adding at least one mapping layer to said neural network and retraining the modified fault classifier, including said mapping layer, with said first plurality of sets of signals acquired by said first plurality of sensors; applying the modified fault classifier to at least one additional set of signals acquired by at least one sensor of the first plurality of sensors and emanating from at least one given mechanical machine sharing the at least one characteristic, the modified fault classifier being configured to automatically identify at least one fault of the at least one given mechanical machine based on the at least one additional set of signals, and providing a human sensible output, by an output device, including at least identification of the fault of the at least one given mechanical machine, at least one of a repair or maintenance operation being performed based on the human sensible output.

In accordance with one preferred embodiment of the present invention, the method also includes following the causing the first plurality of sensors to acquire the first plurality of sets of signals and prior to the supplying the first plurality of sets of signals to the pre-existing fault classifier: obtaining a first plurality of sets of operational condition data for mechanical machines of the first plurality of mechanical machines, each set of operational condition data indicating a state of operation of a mechanical machine of the first plurality of mechanical machines, each state of operation being associated with a least one of the sets of signals, the supplying at least the first plurality of sets of signals to the pre-existing fault classifier also including supplying the operational condition data of the first plurality of mechanical machines to the pre-existing fault classifier, the modifying the pre-existing fault classifier by employing transfer learning, based at least on the first plurality of sets of signals also including modifying the pre-existing fault classifier by employing transfer learning, additionally based on the first plurality of sets of operational condition data of the first plurality of mechanical machines.

Preferably, the identification of the fault includes identification of a specific fault of the at least one given mechanical machine and a prediction of failure of the at least one given mechanical machine due to the specific fault in the absence of performance of a recommended maintenance operation thereupon, wherein the at least one given mechanical machine would indeed fail in the absence of performance of the recommended maintenance operation.

Preferably, the neural network is otherwise unmodified by the modifying, besides the addition of the at least one mapping layer.

Preferably, the neural network including the pre-existing fault classifier includes a data layer and an input layer for receiving data from the data layer, the at least one mapping layer being added between the data layer and the input layer, whereby the at least one mapping layer is configured to receive the data from the data layer in the modified fault classifier.

Preferably, the first plurality of sensors has a first frequency response distribution and the second plurality of sensors has a second frequency response distribution, the mapping layer being configured to map between the first and second frequency response distributions.

In accordance with one preferred embodiment of the method of the present invention, the first plurality of sensors is operative to sense a same type of signal as sensed by the second plurality of sensors.

Preferably, the same type of signal includes one of a vibration signal, a magnetic flux signal, a current, a temperature and an internal machine pressure signal.

In accordance with another preferred embodiment of the present invention, the first plurality of sensors and the second plurality of sensors are operative to sense mutually different types of signals.

Preferably, the mutually different types of signals include at least one of: vibration and magnetic flux signals, vibration and electric current signals, vibration and temperature signals, electric current and magnetic flux signals and vibration and internal machine pressure signals.

Preferably, at least some of the states of operation of a mechanical machine of the first plurality of mechanical machines, as indicated by the first plurality of sets of operational condition data, are states of faulty operation.

In accordance with yet another preferred embodiment of the present invention, the first plurality of sets of signals and the first plurality of sets of operational condition data of the first plurality of mechanical machines include less than <NUM> of the states of faulty operation.

According to a second aspect of the present invention there is provided a system for identifying a fault of at least one mechanical machine, including a first plurality of sensors coupled to a corresponding first plurality of mechanical machines and operative to acquire a first plurality of sets of signals emanating from the first plurality of mechanical machines, the first plurality of mechanical machines sharing at least one characteristic, a data processing unit operative to: receive the first plurality of sets of signals of the first plurality of mechanical machines, the data processing unit including a pre-existing fault classifier comprising a neural network previously trained to automatically classify states of operation of a second plurality of mechanical machines based on signals emanating therefrom and previously acquired by a second plurality of sensors, the second plurality of sensors being of a different type than the first plurality of sensors, the second plurality of mechanical machines sharing the at least one characteristic, modify the pre-existing fault classifier by employing transfer learning, based at least on the first plurality of sets of signals of the first plurality of mechanical machines, thereby providing a modified fault classifier, said data processing unit being operative to modify said pre-existing fault classifier comprising said data processing unit being operative to add at least one mapping layer to said neural network and retrain the modified fault classifier, including said mapping layer, with said first plurality of sets of signals acquired by said first plurality of sensors, and apply the modified fault classifier to at least one additional set of signals acquired by at least one sensor of the first plurality of sensors and emanating from at least one given mechanical machine sharing the at least one characteristic, the modified fault classifier being configured to automatically identify at least one fault of the at least one given mechanical machine based on the at least one additional set of signals, and an output device in communication with the data processing unit and operative to provide a human sensible output including at least identification of the fault of the at least one given mechanical machine, at least one of a repair or maintenance operation being performed based on the human sensible output.

In accordance with one preferred embodiment of the present invention, the system also includes a data collection unit operative to obtain a first plurality of sets of operational condition data for mechanical machines of the first plurality of mechanical machines, each set of operational condition data indicating a state of operation of a mechanical machine of the first plurality of mechanical machines, each state of operation being associated with a least one of the sets of signals, the data processing unit being operative to receive the operational condition data of the first plurality of mechanical machines and to modify the pre-existing fault classifier additionally based on the first plurality of sets of operational condition data of the first plurality of mechanical machines.

Preferably, the neural network is otherwise unmodified by the modification, besides the addition of the at least one mapping layer.

In accordance with one preferred embodiment of the system of the present invention, the first plurality of sensors is operative to sense a same type of signal as sensed by the second plurality of sensors.

In accordance with another preferred embodiment of the system of the present invention, the first plurality of sensors and the second plurality of sensors are operative to sense mutually different types of signals.

In accordance with yet another preferred embodiment of the system of the present invention, the first plurality of sets of signals and the first plurality of sets of operational condition data of the first plurality of mechanical machines include less than <NUM> of the states of faulty operation.

Reference is now made to <FIG>, which is a simplified, high level block diagram illustration of a system for mechanical machine fault identification, constructed and operative in accordance with a preferred embodiment of the present invention.

As seen in <FIG>, there is provided a system <NUM> for mechanical machine fault identification. System <NUM> preferably includes a first plurality of sensors <NUM> of a first type, here indicated as sensor type S1. All of sensors <NUM> may be of the same type e.g. sensor type S1 as shown here. Alternatively, first plurality of sensors <NUM> may include more than one type of sensor e.g. sensors types S1, S11 through to S1N, as shown in <FIG>, described in greater detail henceforth. Sensors <NUM> are preferably coupled to a corresponding first plurality of mechanical machines <NUM>. Here, by way of example, first plurality of mechanical machines <NUM> is shown to include mechanical machines <NUM>, <NUM> through to N, where N may be any number of mechanical machines, such as two or more mechanical machines. Typically, sensors <NUM> are coupled to mechanical machines <NUM> in a one-to-one corresponding arrangement, with one of sensors <NUM> coupled to a corresponding one of machines <NUM>. However, other arrangements are also possible, wherein a single sensor may be arranged to sense signals from more than one of mechanical machines <NUM>. Sensors <NUM> may be physically contacting machines <NUM>, such as directly or indirectly mounted on machines <NUM>. Sensors <NUM> may alternatively be physically separated from machines <NUM>, such as located at a given distance from machines <NUM>, for example in the case that sensors <NUM> are optical sensors.

Sensors <NUM> may be embodied as any type of sensing device operative to sense signals emanating from mechanical machines <NUM>. Machines and mechanical systems with moving parts, such as machines including bearings, rotors or shafts, or motors, engines, compressors, pumps, fans, gear boxes, chillers, etc., may generate signals during the operation thereof. Mechanical machines <NUM> may be of any of the aforementioned types of mechanical systems or of other types of mechanical systems generating signals during the operation thereof. Sensors <NUM> are preferably operative to sense such signals. Analysis of the sensed signals may be used to ascertain a condition of the machine from which the sensed signal emanated and in some cases to ascertain a fault of the machine from which the signal emanated. By way of non-limiting example only, sensors <NUM> may all be the same type of vibration sensors, such as all be single-axis accelerometers or all be multi-axis accelerometers, for sensing vibrations emanating from machines <NUM>; sensors <NUM> may all be the same type of magnetic flux sensors sensing magnetic flux emanating from machines <NUM>; sensors <NUM> may all be the same type of electric current sensors sensing variation in electric currents generated by machines <NUM>; sensors <NUM> may all be the same type of temperature sensors sensing heat generated by machines <NUM>. It is understood that sensors <NUM> may alternatively all be of the same type of any other sort of sensing device, capable of sensing signals emanating from and generated by machines <NUM>, including signals associated with machine operation such as torque, displacement, input line frequency etc.. Sensors <NUM> may alternatively comprise two or more types of sensors, such as, by way of example only, magnetic flux sensors and vibration sensors.

Mechanical machines <NUM> - N which are members of the first plurality of mechanical machines <NUM> are preferably characterized by one or more shared characteristics. Mechanical machines <NUM> - N may or may not be the same machines, provided that they have in common at least one shared characteristic. For example, shared characteristics may refer to type, model number, manufacturer, physical characteristics or dimensions, operating characteristics or parameters, or other shared characteristics that indicate that an observed behavior of one of the mechanical machines of the plurality of mechanical machines may be typical of another mechanical machine of the plurality of mechanical machines.

First plurality of sensors <NUM> is preferably operative to acquire a first plurality of sets of signals emanating from the first plurality of mechanical machines <NUM>. The sets of signals may be 'signal snapshots' sensed by an appropriate one of sensors <NUM> for a short time period. For example, the signal may be sensed for a period of a few seconds, such as one - four seconds. Each set of signals may alternatively comprise multiple 'signal snapshots' over time, for example four second 'signal snapshots' measured each hour over a period of several hours, days or even months. Alternatively, each set of signals may comprise continuously monitored signals over a longer period or more than one period of time. For example, the signal may be monitored every millisecond, continuously.

Signals acquired by sensors <NUM> may be pre-processed, for example by an analog or digital processing capability of the sensor <NUM> itself or by other hardware and/or software signal processing components <NUM>. It is understood that although signal processor <NUM> is shown in <FIG> as a separate element from sensors <NUM>, signal processing functionality may be incorporated within one or more of sensors <NUM>. Signal pre-processing may involve at least one of digitization, compression, feature extraction and representation of the signal in the time or frequency domain.

The sets of signals acquired by first plurality of sensors <NUM> may be uploaded to a remote server, such as a server in the cloud. Signal processing functionality <NUM> may be carried out at the remote server. Preferably, the sets of signals acquired by first plurality of sensors <NUM> are accumulated as they are acquired from the first plurality of machines <NUM>. For example, the sets of signals may be accumulated at the server in the cloud.

System <NUM> may optionally include a data collection unit <NUM>. Data collection unit <NUM> may be operative to receive a first plurality of sets of operational condition data for mechanical machines of the plurality of mechanical machines <NUM>, each set of operational condition data indicating a state of operation of a mechanical machine of the first plurality of mechanical machines <NUM>, each state of operation being associated with a least one of the sets of signals acquired by the first plurality of sensors <NUM>.

The operational condition data collected at data collection unit <NUM> is preferably in the form of machine condition diagnoses supplied by human experts, such as engineers. The sets of signals acquired by first plurality of sensors <NUM> may optionally be provided to data collection unit <NUM>, either by signal processor <NUM> and/or directly or indirectly by sensors <NUM>. Human experts may optionally analyze the sets of signals acquired by the first plurality of sensors <NUM> and label each set of signals of the sets of signals as representing particular states of operation of the corresponding mechanical machine <NUM> from which the signals emanated. The human experts may interact with a user interface, for example of the data collection unit <NUM> or of another device that enables communication between the human expert and the data collection unit <NUM>, to enter the operational state data. The signals and the labels applied thereto may be accumulated and stored in a data base in data collection unit <NUM>. In one embodiment of the present invention, data collection unit <NUM> may be located in a remote server, such as a server in the cloud.

Identification of faults by the human experts may include identification of one or more specific faults of the monitored machine <NUM>. Depending on the specific machine <NUM> being monitored, the specific fault identified may include bearing wear of a rotating machine, mechanical looseness, misalignment, unbalancing, electrical faults or other faults. Identification of faults may alternatively include identification of a machine <NUM> being in a faulty state i.e. an anomalous state with respect to the normal, healthy operating state thereof, but without identifying a specific fault. In this case, the human expert fault identification identifies the machine as not operating in a healthy manner but does not identify what is the specific cause of the unhealthy operation.

Irrespective of whether the particular states of operation represented by the signals are or are not labeled by human experts, the sets of signals acquired by sensors <NUM> may include both sets of signals corresponding to healthy, non-faulty states of operation of machines <NUM> and sets of signals corresponding to unhealthy, faulty states of operation of machines <NUM>.

Alternatively, the sets of signals acquired by sensors <NUM> do not necessarily include sets of signals corresponding to unhealthy faulty states of operation of machines <NUM>. In accordance with this embodiment of the present invention, machines <NUM> being monitored by sensors <NUM> may all be in a healthy operational condition. The sets of signals and optionally associated states of machine operation may therefore all correspond to healthy states of machine operation.

The sets of signals, as accumulated from first plurality of machines <NUM>, are preferably supplied to a pre-existing fault classifier <NUM> included in system <NUM>. In the case that system <NUM> also includes data collection unit <NUM> and the signals are labelled, the labelled signals are preferably combinedly provided by signal pre-processor <NUM> and data collection unit <NUM>, to pre-existing fault classifier <NUM>.

Pre-existing fault classifier <NUM> may be an algorithmic classifier. For example, the pre-existing fault classifier <NUM> may be stored a remote server. System <NUM> may include a non-transitory computer readable storage medium having stored thereupon computer executable instructions for executing, by a processor, the functionality of the pre-existing fault classifier <NUM>. The one or more processors executing pre-existing fault classifier <NUM> may be remote processors, for example located in the cloud, or may be local processors.

Pre-existing fault classifier <NUM> is a fault classifier that has been previously trained to automatically identify faults of a second plurality of mechanical machines based on signals emanating therefrom and previously acquired by a second plurality of sensors, the second plurality of sensors being of a different type than the first plurality of sensors <NUM>. The second plurality of sensors may all be of the same type as each other, which type may be different than the type or types of sensors of first plurality of sensors <NUM>. The second plurality of sensors may alternatively all be of the same type as each other, which type may be different than at least one of the types of sensors of first plurality of sensors <NUM>. The second plurality of sensors may alternatively comprise more than one type of sensor, which types may all be different types than the sensor or sensors of first plurality of sensors. The second plurality of sensors may alternatively comprise more than one type of sensor, which types may be different types than at least one of the types of sensors of first plurality of sensors <NUM>. The second plurality of mechanical machines preferably shares the at least one mechanical characteristic shared by first plurality of mechanical machines <NUM>.

It is understood that fault classifier <NUM> is termed here 'pre-existing' because it may be pre-existing with respect to the sets of signals and optional operational condition data acquired by sensors <NUM> from machines <NUM>. Fault classifier <NUM> may have been previously generated at an earlier point in time, prior to the generation of the data set comprising the sets of signals and optional operational condition data of machines <NUM>.

Preferably, fault classifier <NUM> is an accurate classifier, configured to accurately identify faults in mechanical machines sharing the at least one common characteristic, based on signals acquired by the second plurality of sensors. Fault classifier <NUM> may be such an accurate classifier due to having been previously trained, using machine learning, on a large data set comprising signals and possibly associated operational condition data acquired from a large number of machines. As is well known by those skilled in the art, the greater the volume of data supplied to a machine learning fault classifier for the purpose of training thereof, the more accurately the classifier may perform, up to a given limit. For example, fault classifier <NUM> may have been trained using data sensed from over <NUM>,<NUM> individual rotating machines, including motors, pumps, fans, chillers, compressors and gear boxes. The common characteristic shared by such machines may be the inclusion of bearings therein. An example of how fault classifier <NUM> may have been previously trained in shown in <FIG>, described henceforth.

Pre-existing fault classifier <NUM> may therefore be successfully applied to signals emanating from mechanical machines having a shared characteristic with the mechanical machines based on data from which fault classifier <NUM> was trained, in order to identify faults thereof. However, it is noted that fault classifier <NUM> was previously trained based on signals acquired by a specific type or types of sensor, namely a second plurality of sensors, of a different type than first plurality of sensors <NUM>. As a result, fault classifier <NUM> is capable of classifying and identifying faults most successfully when applied to signals acquired by the specific sensors based on which fault classifier <NUM> was trained. However, in the case that the signals supplied to fault classifier <NUM> are acquired by different types of sensors than the second plurality of sensors based on which fault classifier <NUM> was trained, fault classifier <NUM> will not be capable of accurately classifying and identifying faults based on these signals. This is because of the difference in sensor characteristics between the sensors i.e. the second plurality of sensors, based on which fault classifier <NUM> was trained and the sensors, for example the first plurality of sensors S1 or first plurality of sensors S1 - S1N (<FIG>), having acquired a present signal requiring classification.

Even should fault classifier <NUM> be a highly accurate classifier for identifying faults based on signals acquired by the second plurality of sensors, fault classifier is therefore of limited, if any, use in identifying faults acquired by different types of sensors e.g. first plurality of sensors S1 or S1 - S1N (<FIG>). Should fault classifier <NUM> be applied to the signals acquired by the first plurality of sensors, the results would not be accurate.

This may be exemplified by reference to the case of two types of vibration sensors, such as a tri-axial accelerometer and single-axis accelerometer, sensing vibration signals generated by a mechanical machine. The two types of vibration sensors differ from each other in various parameters such as geometry, mass, internal materials, etc. leading to differences in the moments of inertia and resonance frequencies of the respective sensors. As a result, the sensors have mutually different frequency responses. A particular signal generated by a mechanical machine being monitored will be differently sensed and recorded by the two sensors, due to the innate differences between the sensors. Moreover, if the two sensors are mounted at different locations on the machine being monitored thereby, this difference will be even further exacerbated due to the sensors measuring along mutually different measurement axes and due to different vibration levels measured due to the difference in location.

For example, in the case of a tri-axial accelerometer and a single-axis accelerometer being mounted on a cylindrical machine, the two types of accelerometers will measure mutually different vibration levels, since the tri-axial accelerometer will measure radial vibration along one axis and tangential vibration along two axes whereas the single-axis accelerometer will measure radial, or direct, vibration.

Consequently, the use of a fault detection classifier trained with signals acquired from one type of sensor e.g. vibration signals acquired by a tri-axial accelerometer, will lead to improper classification results when applied to the same type of signals acquired by a different type of sensor e.g. vibration signals acquired by a single-axis accelerometer.

This may be further exemplified by reference to a more extreme case of two types of sensors sensing different types of signals, such as a vibration sensor and a magnetic flux sensor, respectively sensing vibration and magnetic flux signals emanating from a particular mechanical machine. A fault detection classifier trained using data acquired by one of the types of sensors e.g. vibration signals acquired by the vibration sensor, will be limited to classifying vibration signals and will provide poor results of little relevance if applied to identify faults in, for example, magnetic flux signals generated by the same machine.

In order to provide a fault classifier capable of accurately classifying signals acquired by a different type of sensor to that based on which the fault classifier was previously trained, an entirely new fault classifier may be trained based on signals acquired by the different type of sensor e.g. first plurality of sensors S1 or S1, S11 through to S1N (<FIG>). In this case, the pre-existing fault classifier <NUM> is not made use of and a new fault classifier is developed in order to identify faults of mechanical machines such as machines <NUM>. However, in order for this new classifier to provide accurate fault identification, a large volume of new data acquired by the different type of sensors e.g. first plurality of sensors S1 or first plurality of sensors S1 - S1N (<FIG>), must be supplied thereto and the fault classifier must be trained based on this. Such a process may be lengthy and such a large volume of data may not be available. Additionally, such a process is also highly limited in performance and scope of applicability, due to the many parameters controlling the frequency dependence of machine signals, such as sensor mounting location, orientation, mounting type etc.. Furthermore, in this approach, the capability of the original pre-existing fault classifier <NUM> is simply wasted, rather than harnessed, since the previous fault classifier <NUM> is not applied at all.

The present invention advantageously provides a solution to the problem of a fault classifier trained on data acquired from a certain type or types of sensor being of limited, if any, use in classifying signals acquired from a different type or types of sensor, due to the difference in sensor characteristics. Advantageously, the present invention does not require the training 'from scratch' of a new classifier based on signals from the different type of sensor. Rather, the present invention makes use of a transfer learning approach for mapping between the original sensor type(s) based on which the pre-existing classifier was trained and the new, different sensor type(s) from which new data, requiring classification, is obtained.

The present invention may utilize the pre-existing classifier <NUM> by modifying the pre-existing classifier <NUM> based on mapping between the different sensor frequency response distributions of the respective different sensor types and requires only a small data set from the different type of sensors e.g. first plurality of sensors <NUM>, in order to perform such mapping and modification. In a preferred embodiment of the present invention, a modified classifier may thus be generated, based on the original pre-existing classifier <NUM> and a small new data set acquired from a type or types of sensor different than the type or types of sensor on which the pre-existing classifier <NUM> was based. This modified classifier may be capable of accurately identifying faults in signals acquired by the different type of sensors e.g. first plurality of sensors <NUM>, despite the small data set supplied thereto. The modified classifier harnesses the original pre-existing classifier <NUM> and maps it to the different type of sensors e.g. first plurality of sensors <NUM>, so as to be accurately applicable to the signals acquired by the different type of sensors.

It is noted, however, that the present invention may be of use even in the case of the availability of a large, high quality data set from the different type of sensors e.g. first plurality of sensors <NUM>. Although in this case, since a large, high quality data set is available a new dedicated classifier may be trained to provide adequate results, the use of transfer learning to modify a pre-existing classifier may still be advantageous, in order to take advantage of the capabilities of the original pre-existing classifier. Thus, although the present invention is expected to be most useful cases where sufficient data, in terms of quantity and/or quality, is not available in order to train a new classifier, the present invention may also be useful in the case that a large, high quality data set is available.

In the case of a pre-existing accurate fault classifier trained using signals acquired by a specific type or types of sensors, the present invention thus provides a solution for modifying the classifier so as to capable of accurately classifying signals acquired by any other type or types of sensors different from the specific type or types of sensors based on which the fault classifier was trained, where these signals emanate from machines have at least one shared characteristic with those machines based on which the classifier was previously trained. This may be termed a sensor-agnostic approach, where the classifier may be calibrated so as to be capable of being applied to data acquired by any sensor, regardless of the type and/or structure of the source data collected by the sensor.

The approach of the present invention may be applicable in the case of the same type of signal e.g. vibration signals, acquired by different types of vibration sensors. Results for this are shown in <FIG>. The approach of the present invention may also be applicable in the case of a different type of signal e.g. vibration and magnetic signals, acquired by different types of sensors, e.g. vibration sensors and magnetic flux sensors. Results for this are shown in <FIG>.

In both cases, the original pre-existing classifier may be modified based on mapping between the sensor frequency response distributions, in order to create a modified classifier capable of identifying faults in the signals acquired by the sensors of a different type than those based on which the pre-existing classifier was trained. In both cases the modified classifier may be applied to the signals acquired by the sensors of a different type than those based on which the pre-existing classifier was trained, with a greater accuracy than would be achieved by applying the pre-existing classifier in its original, unmodified form. Furthermore, in both cases the modified classifier may be applied to the signals acquired by the sensors of a different type than those based on which the pre-existing classifier was trained, with a greater accuracy than would be achieved by applying a new classifier, trained using only the signals acquired by the sensors of a different type.

The acquisition of a small new data set from the different type of sensors e.g. first plurality of sensors <NUM>, has been described hereinabove with respect to sensors <NUM> acquiring signals and optional associated operational condition data from machines <NUM>. The acquisition of a small data set by the system of <FIG> is preferably carried out by those elements enclosed in a dashed box <NUM>.

In the embodiment of the invention shown in <FIG>, first plurality of sensors <NUM> is preferably all the same type of sensor ie. sensor type S1. However, as mentioned previously, first plurality of sensors <NUM> may alternatively include more than one type of sensor. <FIG> shows an alternative embodiment of system <NUM>, here indicated as system 100A, showing the inclusion in first plurality of sensors <NUM> of more than one type of sensor. Turning now to <FIG>, first plurality of sensors <NUM> may include sensors S1, S11 to S1N on each of machines <NUM> - N of plurality of machines <NUM>. It is understood that first plurality of sensors <NUM> may include any number of sensors S1 through to S1N, such as two or more sensors. These sensors may be, for example, a combination of vibration sensors, magnetic flux sensors, current sensors, temperature sensors, or sensors for sensing other parameters associated with the operation of machines <NUM>, such as torque, displacement, input line frequency etc.. Sensors S1, S11 through to S1N may be of mutually different types to each other, but are preferably of the same types with respect to the sets of sensors coupled to each of machines <NUM>. However, it is understood that system 100A may tolerate the case of some missing data from ones of the sensors <NUM>, which missing data may be imputed, for example, by using multivariate statistics.

System 100A may generally resemble system <NUM> with the exception of the multiple types of sensors included in first plurality of sensors <NUM> in system 110A, and the description of system <NUM> generally also applies to system 100A.

With continued reference to <FIG> and <FIG>, the small new data set, based on which the pre-existing classifier <NUM> may be modified by mapping learning to provide a modified classifier, may include much less data than the amount of data based on which pre-existing classifier <NUM> was originally trained. For example, the small new data set may comprise less than <NUM>, less than <NUM>, less than <NUM>, less than <NUM>, less than <NUM> or less than <NUM> sets of each of the first plurality of sets of signals and optionally the first plurality of sets of operational condition data of the first plurality of mechanical machines <NUM>. Furthermore, within the small new data set may be an even smaller number of sets of signals corresponding to a state of faulty operation of a mechanical machine of first plurality of mechanical machines <NUM>, such as less than <NUM>, less than <NUM>, less than <NUM>, less than <NUM>, less than <NUM>, less than <NUM>, less than <NUM> or less than <NUM> sets of signals corresponding to a state of faulty operation of a mechanical machine of first plurality of mechanical machines <NUM>. As mentioned above, in some cases the small new data set may not even include signals corresponding to a state of faulty operation of a mechanical machine of first plurality of mechanical machines <NUM>. This is in contrast to a much larger data set based on which the pre-existing classifier <NUM> may have been trained, such as several thousand samples.

It is appreciated that first plurality of sensors <NUM> is different from the second plurality of sensors based on which pre-existing classifier <NUM> was trained, but that machines <NUM> do preferably share a common characteristic both with each other and with the machines based on which pre-existing classifier <NUM> was trained. Machines <NUM> may be the same, or may not be the same, as those machines based on which the pre-existing classifier <NUM> was previously trained.

The first plurality of sets of signals acquired by first plurality of sensors <NUM> and optionally the first plurality of sets of operational condition data collected at data collection unit <NUM> of the first plurality of mechanical machines <NUM> may be supplied to pre-existing fault classifier <NUM>. The first plurality of sets of signals may be pre-processed by signal processor <NUM> prior to the provision thereof to pre-existing fault classifier <NUM>.

Pre-existing fault classifier <NUM> is then preferably modified based on the plurality of sets of signals and optionally the plurality of sets of operational condition data of the first plurality of mechanical machines <NUM>, thereby producing a modified classifier <NUM>.

Modified classifier <NUM> may be an algorithmic classifier executable by one or more processors, which may be the same or different processors as those executing pre-existing classifier <NUM>. For example, pre-existing classifier <NUM> and modified classifier <NUM> may be embodied within a data processing unit. The one or more processors executing pre-existing fault classifier <NUM> may be remote processors, for example located in the cloud, or may be local processors. For example, the one or more processors executing modified classifier <NUM> may be located within ones of sensors <NUM>.

Pre-existing fault classifier <NUM> is modified by adjusting the classifier to the new data set comprising the plurality of sets of signals acquired by sensors <NUM> and optionally the plurality of sets of operational condition data of the first plurality of mechanical machines <NUM>. For example, as seen in <FIG>, in the case that first plurality of sensors <NUM> includes only sensor type S1, modified classifier <NUM> may be adapted to sensor type S1. Further by way of example, as seen in <FIG>, in the case that first plurality of sensors includes multiple sensor types S1, S11 etc, modified classifier <NUM> may be adapted to those multiple sensor types. By adjusting the pre-existing classifier <NUM> to produce a modified classifier <NUM>, the classifier is calibrated so as to be applicable to a different data set than that data set based on which the classifier was originally trained. The adjustment involves mapping between the original sensor characteristics based on which the classifier <NUM> was previously trained and the sensor characteristics of first plurality of sensors <NUM>.

The adjustment may involve mapping between the sensor frequency response distribution of the original plurality of sensors based on which the classifier <NUM> was previously trained and the sensor frequency response distribution of first plurality of sensors <NUM>. The mapping may be between sensor frequency response distributions, rather than simply sensor frequency responses, since the frequency responses both of the sensors based on which classifier <NUM> was previously trained and of the first plurality of sensors <NUM> may be distributed. This distribution may arise due to real world variation between the sensors within each plurality, such as variation in the machine characteristics, variation in the sensor location, sensor mounting, exact type of sensor mounting etc..

The mapping may be carried out in a supervised manner. In this embodiment, data collection unit <NUM> may be included in system <NUM> and labelled signals are supplied to pre-existing fault classifier <NUM>. Further details pertaining to how pre-existing fault classifier <NUM> is modified by mapping between sensors in a supervised manner are provided henceforth with reference to <FIG>.

The mapping may alternatively be carried out in an unsupervised manner. In this case, data collection unit <NUM> need not be included in system <NUM> and signals without associated operational states are supplied to pre-existing fault classifier <NUM>. Pre-existing fault classifier <NUM> may be modified by unsupervised learning to map differences between the sensor frequency response distributions of the sensors based on which classifier <NUM> was previously trained and the new sensors e.g. first plurality of sensors <NUM>.

The mapping may alternatively be carried out in a semi-supervised manner, wherein labelled signals are supplied to pre-existing fault classifier <NUM> and mapping between sensors in order to produce modified classifier <NUM> is carried out in an unsupervised manner. Further details relating to unsupervised mapping are provided henceforth, with reference to <FIG>.

Modified classifier <NUM>, having been adapted by mapping so as to be applicable to data from first plurality of sensors <NUM>, is now ready for use for classifying signals acquired by sensors of the same type or types as first plurality of sensors <NUM>.

An example of the employment of modified classifier <NUM> for identifying faults in signals acquired by a sensor of the same type as first plurality of sensors <NUM> is further shown in <FIG> and <FIG>. As seen in <FIG> and <FIG>, at least one sensor <NUM> e.g. sensor S1 in <FIG> and sensors S1 - S1N in <FIG>, may acquire at least one set of signals emanating from at least one given mechanical machine, here depicted as mechanical machine X, indicated by a reference number <NUM>. Mechanical machine <NUM> may share at least one characteristic with first plurality of mechanical machines <NUM>, as well as with the plurality of mechanical machines based on signals from which pre-existing fault classifier <NUM> was trained. Mechanical machine <NUM> may or may not be a member of first plurality of mechanical machines <NUM>. It is understood that although mechanical machine <NUM> is shown here to be embodied as a single machine, this is for the sake of simplicity only, and system <NUM> may include any number of mechanical machines <NUM>, such as one, two or more mechanical machines <NUM>, which may or may not be the same as each other, provided that the machines <NUM> share the at least one characteristic, as described above.

At least one sensor <NUM> is one of first plurality of sensors <NUM>, meaning that sensor <NUM> is of the same type as first plurality of sensors <NUM>. As shown in <FIG>, in the case that first plurality of sensors <NUM> includes a single type of sensor i.e. sensor S1, sensor <NUM> is also of sensor type S1. As shown in <FIG>, in the case that first plurality of sensors <NUM> includes multiple types of sensor i.e. sensors S1 to S1N, at least one sensor <NUM> also comprises the same multiple types of sensors i.e. sensors S1 - S1N. At least one sensor <NUM> is preferably operative to acquire at least one set of signals emanating from given mechanical machine <NUM>. The sets of signals may be 'signal snapshots' sensed by at least one sensor <NUM> for a short time period. For example, the signal may be sensed for a period of a few seconds, such as one - four seconds. The sets of signals may alternatively comprise multiple 'signal snapshots' over time, for example four second 'signal snapshots' measured each hour over a period of several hours, days or even months. Alternatively, the sets of signals may comprise continuously monitored signals over a longer period or more than one period of time. For example, the signal may be monitored every millisecond, continuously.

Signals acquired by at least one sensor <NUM> may be pre-processed, for example by an analog or digital processing capability of the sensor <NUM> itself or by other hardware and/or software signal processing components <NUM>. It is understood that although signal processor <NUM> is shown in <FIG> and <FIG> as a separate element from at least one sensor <NUM>, signal processing functionality may be incorporated within one or more of sensors <NUM>. Signal pre-processing may involve at least one of digitization, compression, feature extraction and signal representation in the time or frequency domain.

The signals acquired by at least one sensor <NUM> may be uploaded to a remote server, such as a server in the cloud. Signal processing functionality <NUM> may be carried out at the remote server.

The set of signals acquired by at least one sensor, here embodied by way of example as sensor <NUM>, emanating from at least one given mechanical machine, here embodied by way of example as mechanical machine X, may be provided to modified classifier <NUM>. Modified classifier <NUM> may be configured to automatically identify at least one fault of mechanical machine X, based on the set of signals emanating therefrom. It is understood that modified classifier <NUM> may be accurately applied to signals acquired by at least one sensor <NUM>, due to modified classifier <NUM> having been adapted to classify signals acquired by sensors of the type of first plurality of sensors <NUM>.

Identification of faults by modified classifier <NUM> may include identification of one or more specific faults of the monitored machine <NUM>. Depending on the specific machine <NUM> being monitored, the specific fault identified may include bearing wear of a rotating machine, mechanical looseness, misalignment, unbalancing electrical faults or other faults.

Identification of faults may alternatively include identification of machine X being in a faulty state i.e. an anomalous state with respect to the normal, healthy operating state thereof, but without identifying a specific fault. In this case, the fault identification identifies the machine as not operating in a healthy manner but does not identify what is the specific cause of the unhealthy operation.

Systems <NUM> and 100A additionally include an output device <NUM>. Output device <NUM> may be operative to receive the fault identification output by modified classifier <NUM> and to provide a human sensible output including at least identification of the fault of at least one given mechanical machine <NUM>. The human sensible output may include at least one of a visual, tactile, or audible output. Preferably, at least one of a repair or maintenance operation is performed based on said human sensible output.

For example, in the case that modified classifier <NUM> is executable by a remote processor, the fault identification output by modified classifier <NUM> may be communicated to output device <NUM>.

Output device <NUM> may also be operative to provide a prediction of failure of at least one given mechanical machine <NUM> due to the fault identified by modified classifier <NUM>, in the absence of performance of a recommended maintenance operation thereupon, wherein at least one given mechanical machine <NUM> would indeed fail in the absence of performance of the recommended maintenance operation.

In some cases, maintenance <NUM> may be performed upon given machine <NUM>, responsive to the human sensible output provided by output device <NUM>. Output device <NUM> may optionally be operatively coupled to a controller of machine <NUM> and operation of machine <NUM> may be adjusted responsive to the fault identification. For example, machine <NUM> may be switched off, may be operated at reduced power, or otherwise adjusted. Such adjustment may be automatic, or may be directed by a human expert in response to the human sensible output provided by output device <NUM>.

Further details of pre-existing classifier <NUM> itself and how pre-existing classifier <NUM> may be adapted or calibrated in order to produce modified classifier <NUM> are now provided with reference to <FIG>, which are respectively a simplified illustration of modification of a classifier, as carried out by either of the systems of the types shown in <FIG> and <FIG> and simplified respective block diagram illustrations of components of two possible systems for the training of a classifier employed in the system of <FIG> or <FIG>. The systems <NUM> and 100A may include a non-transitory computer-readable storage medium having stored thereon computer-executable instructions for executing, by one or more processors, the method of modification of a classifier, as detailed hereinbelow with respect to <FIG> and <FIG>. Such processors may be remote processors or local processors.

As seen in <FIG>, pre-existing fault classifier <NUM> is an artificial neural network (ANN) classifier <NUM> for supervised fault and anomaly detection. For example, the ANN <NUM> may be configured to identify a specific fault or performance anomaly in machines having a rotating component. Without loss of generality, fault classifier <NUM> having a multilayer perceptron architecture is illustrated in <FIG>. However, it is understood the systems and methods may be applied to any type of fault classifier regardless of the structure thereof. More particularly, the numbers layers and neurons shown in <FIG> are by way of highly simplified example only, for the purpose of illustration of the principles of the present invention. Furthermore, pre-existing fault classifier <NUM> may be an unsupervised fault classifier, as described hereinabove, such as an autoencoder, deep belief network, a classifier based on clustering, K-means or hidden Markov models, or any other type of model capable of carrying out unsupervised learning.

An upper panel <NUM> in <FIG> shows an example of pre-existing classifier <NUM> in the original unmodified form thereof, following the training thereof and prior to any modification thereof for the purpose of mapping between sensors types, in accordance with the present invention.

Pre-existing classifier <NUM> has a multi-layer architecture. A data layer <NUM> may be the layer comprising the input data, in the form of sets of sensor signals. An input layer <NUM> may be an initial layer at which sensor signal sets are input into the network <NUM>. At each of a multiplicity of subsequent hidden layers <NUM> the signal sets are fused with respective weightings and an activation function applied for the combined layer output, before forwarding it to the next layer of the hidden layers <NUM>. This process repeats itself for each layer of the hidden layers <NUM>, until an output layer <NUM> is reached. Output layer <NUM> yields a fault score. This score represents identification of a fault. Identification of a fault may include identification of a present particular fault or anomaly or prediction of a future impending particular fault. A fault may include any type of machine anomaly.

A general mathematical expression for the ANN architecture shown in panel <NUM> of <FIG> within the network layers is <MAT> where s is the neuron input, which for the first layer <NUM> is the sensor signal, σ is the activation function, ω is the weight, b is a bias term and i and j are indices that run on the layer incoming data points and neurons, respectively.

Training of network <NUM> in order to generate pre-existing classifier <NUM> may be better understood with additional reference to <FIG> and <FIG>.

Turning initially to <FIG>, a system <NUM> for the training of pre-existing classifier <NUM> may include a second plurality of sensors <NUM>. Sensors <NUM> are preferably coupled to a corresponding second plurality of mechanical machines <NUM>. Second plurality of sensors <NUM> may be all of a same, second type, here indicated as sensor type S2. Alternatively, sensors <NUM> may include multiple types of sensors coupled to each corresponding one of second plurality of mechanical machines <NUM>. The case of second plurality of sensors <NUM> including multiple types of sensors is shown in a system 400A of <FIG>. System 400A generally resembles system <NUM> with the exception of the inclusion of multiple types of sensors <NUM> therein. Turning now to <FIG>, it is understood that second plurality of sensors <NUM> shown in <FIG> may include any number of sensors S2 through to S2N, such as two or more sensors. These sensors may be, for example, a combination of vibration sensors, magnetic flux sensors, current sensors, temperature sensors, or sensors for sensing other parameters associated with the operation of machines <NUM>, such as torque, displacement, input line frequency etc.. Sensors S2 - S2N may be of mutually different types to each other, but are preferably of the same types with respect to each of the sets of sensors coupled to each of machines <NUM>.

Sensors <NUM>, e.g. sensor S2 or sensors S2 - S2N, may be different than first plurality of sensors <NUM> of <FIG> or different than sensor types S1 - S1N of first plurality of sensors <NUM> of <FIG>. Any combination of the embodiments shown in <FIG> and <FIG> and <FIG> and <FIG> are possible i.e. first plurality of sensors <NUM> may include only one type of sensor S1 and second plurality of sensors <NUM> may include only one type of sensor S2, which is different than S1 (embodiment of <FIG> and <FIG>); first plurality of sensors <NUM> may include only one type of sensor S1 and second plurality of sensors <NUM> may include multiple types of sensors S2 - S2N, at least some of which are different than S1 (embodiment of <FIG> and <FIG>); first plurality of sensors <NUM> may include multiple type of sensors S1 - S1N and second plurality of sensors <NUM> may include only one type of sensor S2, which is different than at least some of S1 - S1N (embodiment of <FIG> and <FIG>); first plurality of sensors <NUM> may include multiple types of sensors S1 - S1N and second plurality of sensors <NUM> may include multiple types of sensor S2 - S2N, at least some of which are different than S1 - S1N (embodiment of <FIG> and <FIG>).

In some embodiments, first and second plurality of sensors <NUM> and <NUM> may include some sensors in common e.g. first plurality of sensors <NUM> may include vibration and magnetic sensors and second plurality of sensors <NUM> may include only magnetic sensors.

Here, by way of example, second plurality of mechanical machines <NUM> is shown to include mechanical machines <NUM>, <NUM> through to N, where N may be any number of mechanical machines, such as two or more mechanical machines. Typically, sensors <NUM> are coupled to mechanical machines <NUM> in a one-to-one corresponding arrangement, with one of sensors <NUM> coupled to a corresponding ones of machines <NUM>. However, other arrangements are also possible, where a single sensor may be arranged to sense signals from more than one of mechanical machines <NUM>. Sensors <NUM> may be physically contacting machines <NUM>, such as directly or indirectly mounted on machines <NUM>. Sensors <NUM> may alternatively be physically separated from machines <NUM>, such as located at a given distance from machines <NUM>, for example if sensors <NUM> are optical sensors.

The sensors of second plurality of sensors <NUM> may have generally the same frequency response as each other. The frequency response of second plurality of sensors <NUM> may be termed the frequency response distribution of sensors <NUM>. First plurality of sensors <NUM> also may have generally the same frequency response as each other. The frequency response of first plurality of sensors <NUM> may be termed the frequency response distribution of sensors <NUM>. Due to sensors <NUM> being of a different type than sensors <NUM>, the frequency response distribution of first plurality of sensors <NUM> is different than the frequency response distribution of second plurality of sensors <NUM>. For example, first plurality of sensors <NUM> may be single-axis vibration sensors and second plurality of sensors <NUM> may be multi-axis vibration sensors, or vice versa, or first plurality of sensors may be single axis vibration sensors and second plurality of sensors may be magnetic flux sensors. Sensors <NUM> and <NUM> or sensors <NUM> and <NUM> may respectively measure vibration and magnetic flux signals; vibration and electric current signals; vibration and temperature signals; electric current and magnetic flux signals; and vibration and internal machine pressure signals, by way of example only.

Sensors <NUM> are preferably operative to sense signals emanating from mechanical machines <NUM>. Mechanical machines <NUM> - N which are members of the second plurality of mechanical machines <NUM> are preferably characterized by one or more shared characteristics both with each other and with mechanical machines <NUM>. Mechanical machines <NUM> - N may or may not be the same machines, provided that they have in common at least one shared characteristic. For example, shared characteristics may refer to type, model number, manufacturer, physical characteristics or dimensions, operating characteristics or parameters, or other shared characteristics that indicate that an observed behavior of one of the mechanical machines of the plurality of mechanical machines may be typical of another mechanical machine of the plurality.

Second plurality of sensors <NUM> is preferably operative to acquire a second plurality of sets of signals emanating from the second plurality of mechanical machines <NUM>. The sets of signals may be 'signal snapshots' sensed by an appropriate one of sensors <NUM> for a short time period. For example, the signal may be sensed for a period of a few seconds, such as one - four seconds. Each set of signals may alternatively comprise multiple 'signal snapshots' over time, for example four second 'signal snapshots' measured each hour over a period of several hours, days or even months. Alternatively, each set of signals may comprise continuously monitored signals over a longer period or more than one period of time. For example, the signal may be monitored every millisecond, continuously.

Signals acquired by sensors <NUM> may be pre-processed, for example by an analog or digital processing capability of the sensor <NUM> itself or by other hardware and/or software processing components <NUM>. For example, the signal may be at least one of digitized, compressed, features may be extracted from the signal and signals may be represented in the time or frequency domain.

Systems <NUM> and 400A may optionally include a data collection unit <NUM>. Data collection unit <NUM> is preferably operative to receive a second plurality of sets of operational condition data for mechanical machines of the second plurality of mechanical machines, each set of operational condition data indicating a state of operation of a mechanical machine of said second plurality of mechanical machines <NUM>, each state of operation being associated with a least one of the sets of signals acquired by the second plurality of sensors <NUM>.

The operational condition data collected at data collection unit <NUM> is preferably in the form of machine condition diagnoses supplied by human experts, such as engineers. The sets of signals acquired by first plurality of sensors <NUM> may optionally be provided to data collection unit <NUM>, either by signal processor <NUM> and/or directly or indirectly by sensors <NUM>.

These human experts may analyze the sets of signals acquired by the second plurality of sensors <NUM> and label each set of signals of the sets of signals as representing particular states of operation of the corresponding mechanical machine <NUM> from which the signals emanated. The human experts may interact with a user interface, for example of the data collection unit <NUM> or of another device that enables communication between the human expert and the data collection unit, to enter the operational state data. The signals and the labels applied thereto may be accumulated and stored in a data base in data collection unit <NUM>. In one embodiment of the present invention, data collection unit <NUM> may be located in a remote server, such as a server in the cloud.

Identification of faults by the human experts may include identification of one or more specific faults of the monitored machines <NUM>. Depending on the specific machine <NUM> being monitored, the specific fault identified may include bearing wear of a rotating machine, mechanical looseness, misalignment, unbalancing, electrical faults or other faults. Identification of faults may alternatively include identification of a machine <NUM> being in a faulty state i.e. an anomalous state with respect to the normal, healthy operating state thereof, but without identifying a specific fault. In this case, the human expert fault identification identifies the machine as not operating in a healthy manner but does not identify what is the specific cause of the unhealthy operation.

It is to be understood that in this example, systems <NUM>/400A preferably include data collector <NUM> and signals are preferably labeled by human experts, in order to train fault classifier <NUM> in a supervised manner. However, in other embodiments of the present invention, data collector <NUM> may be omitted and signals need not be labeled by human experts. In this case, pre-existing fault classifier <NUM> may be originally trained by using unsupervised learning.

Returning now to panel <NUM> of <FIG>, network <NUM> may be trained by supplying thereto the sets of signals acquired by the second plurality of sensors <NUM> as the input data at layer <NUM> and supplying thereto the associated operational state data, as labeled by the human experts, as the required corresponding output at output layer <NUM>. Training of the network <NUM> may be carried out using back-propagation and gradient descent algorithms with respect to pre-defined data labeling. The training parameters, such as loss function, learning rate, optimizer type etc. are preferably chosen with respect to the output score. For example, for a binary fault detection a cross-entropy loss function, Adam optimizer and L2 regularization term may be selected. The training process is first applied for the data set for which all sensors <NUM> are consistent within the entire data set, meaning that there is preferably no mixing between sensors <NUM> for each vector in the data point.

Once the training is completed, the parameters of the network <NUM> established based on the training are preferably fixed. These parameters include activation functions and weights. Network <NUM> now constitutes a pre-existing classifier, such as pre-existing classifier <NUM>, based on sensor signal sets acquired by second plurality of sensors <NUM>. Pre-existing classifier <NUM>, in the form of trained network <NUM> shown in panel <NUM>, is now configured to accurately classify new input data having the same or similar structure and acquired from the same or similar sources as the data based on which the pre-existing classifier <NUM> was trained. In this case, pre-existing classifier <NUM> in the form of trained network <NUM> is configured to accurately classify new input signals emanating from machines have a shared characteristic with machines <NUM> (<FIG> and <FIG>) and sensed by sensors of the same type as second plurality of sensors <NUM> (<FIG> and <FIG>). However, as detailed hereinabove, pre-existing classifier <NUM> in the form of trained network <NUM> is not capable of accurately classifying new input signals sensed by sensors of a different type than second plurality of sensors <NUM>, such as first plurality of sensors <NUM> (<FIG> and <FIG>), despite these new input signals emanating from machines having a shared characteristic with machines <NUM>, such as first plurality of machines <NUM>. In this case, the classifier accuracy will be considerably reduced to the difference in frequency responses of the different types of sensors.

In order to render pre-existing classifier <NUM> capable of accurately classifying signal sets from a different type of sensor e.g. first plurality of sensors <NUM>, than those based on which the classifier was previously trained, e.g. second plurality of sensors <NUM>, pre-existing classifier <NUM> may be modified. Modification of pre-existing classifier <NUM>, in the form of network <NUM>, in accordance with a preferred embodiment of the present invention is shown in a lower panel <NUM> of <FIG>.

As seen in lower panel <NUM> of <FIG>, network <NUM> may be modified to produce a modified network <NUM>. Network <NUM> is preferably modified by adding at least one additional layer <NUM> to network <NUM>. The at least one additional layer <NUM> may be termed a 'mapping layer' and is configured to learn the frequency response difference between the original sensor type i.e. second plurality of sensors <NUM>, based on which classifier <NUM> in the form of network <NUM> was trained, and the new sensor type i.e. first plurality of sensors <NUM>, from which new data has been collected.

The configuration of mapping layer <NUM> may be achieved by freezing the structure and parameters of all of the layers of network <NUM> and retraining the classifier with the new data set acquired from first plurality of sensors <NUM>, as provided by those elements enclosed in dashed box <NUM> described hereinabove with respect to <FIG> and <FIG>. In this way, the network <NUM> is forced to optimize weight values of mapping layer <NUM> with respect to the frequency response difference of the two sensors, since the parameters of all of the other layers of the network <NUM> are already configured and frozen and cannot be changed. As a result, mapping layer <NUM> is forced to learn mapping between the original sensor signal sets provided by sensors <NUM> (<FIG> and <FIG>) and the new sensor signal sets provided by sensors <NUM> (<FIG> and <FIG>).

Alternatively, the structure and parameters of all of the layers of network <NUM> need not necessarily be frozen and rather may be adjusted during the retraining of the classifier with the new data set acquired from first plurality of sensors <NUM>. In this case, the parameters of network <NUM> serve as a starting point for the adjusted parameters of modified classifier <NUM>.

The configuration of mapping layer <NUM> is preferably chosen with respect to the nature of the frequency response difference between the different types of sensors e.g. first and second plurality of sensors <NUM> and <NUM>.

In the case of mapping between different sensors of the same type e.g. first and second plurality of sensors <NUM> and <NUM> are both vibration sensors, but of different types having different frequency responses, the frequency response difference is generally a linear function of the signal frequency.

In the case of mapping between different sensors of different types e.g. first and second plurality of sensors <NUM> and <NUM> are respectively vibration and magnetic sensors having different frequency responses, the frequency response difference is generally a non-linear function and non-linear mapping is therefore required. Correspondingly, the activation function may, but does not necessarily, take a non-linear form.

In some cases, the mapping of mapping layer <NUM> may be assisted by providing to mapping layer <NUM> more complex forms of the new sensor data e.g. more complex forms of the signals acquired by first plurality of sensors <NUM>. For example, inverse data, logarithmic data or other forms of data may be provided. The process of modifying the input signal may be optimized with respect to the classifier accuracy, whereby the new sensor data is modified per classifier accuracy performance feedback and mapping layer <NUM> is then retrained with the modified data.

Mapping layer <NUM> is preferably incorporated into network <NUM> as the first layer after data layer <NUM> and is configured as such to receive the input data in the form of the new data set. Mapping layer <NUM> is upstream from the original input layer <NUM> and precedes the original input layer <NUM> with respect to the input data. The location of mapping layer <NUM> in network <NUM> is important, because it is the location of mapping layer <NUM> as the initial layer in the network that allows mapping layer <NUM> to learn the mapping between the sensor types and adapt the incoming new data to be in an appropriate form for continuing to the other hidden layers <NUM> downstream in network <NUM>.

In an alternative embodiment of the present invention, the pre-existing fault classifier may be originally trained in an unsupervised manner by providing a large quantity of sensor data thereto, in order for the classifier to learn how to identify anomalies in the sensor data relating to machine operating condition. For example, an auto-encoder NN may be used to learn a low-dimensionality representation of the sensor data and clustering based classification then used to identify outliers (anomalies). Such a pre-existing unsupervised fault classifier <NUM> may then be modified in an unsupervised way, in accordance with a preferred embodiment of the present invention, in order to produce modified fault classifier <NUM> adapted to identify anomalies in sensor data acquired by a different type of sensor than that based on which pre-existing fault classifier <NUM> was originally trained. Modified fault classifier <NUM> may be produced, for example, by adding an initial mapping layer to the auto-encoder NN, freezing parameters of all layers of the network besides for the mapping layer and training the mapping layer to learn the mapping between the original and new sensor types. During the training of the mapping layer, another mapping layer, such as an inverse mapping layer, may additionally be added at the NN output, after the decoder, for the sake of the training process. The output may then be classified using clustering based classification in order to identifier outliers (anomalies).

The improvement in classifier performance as a result of the mapping learning of the present invention is illustrated in the following exemplary graphs:
In <FIG>, an example is shown of the performance, in the form of a precision-recall curve, of the modified classifier of the present invention. In this example, a fault classifier was originally trained and validated on a data set comprising over <NUM>,<NUM> labeled signal sets, of which <NUM> signal sets indicated machine faults, acquired from rotating machines including bearings, by single-axis vibration sensors. Following training, the fault classifier was tested on a data set comprising <NUM>,<NUM> signal sets, <NUM>,<NUM> of which corresponded to machine faults. The fault classifier thus trained may be termed the pre-existing fault classifier.

The pre-existing classifier was then modified in accordance with a preferred embodiment of the present invention, in order to be rendered capable of classifying faults based on signals acquired by tri-axial vibration sensors from rotating machines including bearings. The new data set of signals acquired by tri-axial vibration sensors and used to modify the pre-existing fault classifier comprised a training and validation data set of <NUM> signal sets, of which only <NUM> sets of signals were associated with faulty machine states, namely bearing wear. The pre-existing fault classifier was modified, as described above, based on this very small signal set.

The modified fault classifier was then applied to a test set of <NUM>,<NUM> signals sets, of which <NUM> signal sets were associated with faulty machine states, of signals acquired by tri-axial vibration sensors from rotating machines including bearings for fault detection, in order to test the performance thereof (line <NUM>). For the sake of comparison, an entirely new classifier was trained 'from scratch' with the same <NUM> sets of signals and applied to the same test set of <NUM>,<NUM> signal sets. (line <NUM>). For the sake of completeness of comparison, the original pre-existing classifier in its unmodified form was also applied to the <NUM> example set of data (line <NUM>). As is clear from consideration of <FIG>, the performance of the modified classifier in fault identification is the best, despite the extremely small data set supplied. The scores listed in <FIG> are average precision scores, although other example scores may be used.

In <FIG>, an example is shown of the performance, in the form of a precision-recall curve, of the modified classifier of the present invention. In this example, a fault classifier was originally trained and validated on a data set comprising over <NUM>,<NUM> labeled signal sets, of which <NUM>,<NUM> signal sets indicated machine faults, acquired from rotating machines including bearings, by single-axis vibration sensors. Following training, the fault classifier was tested on a data set comprising <NUM>,<NUM> signal sets, <NUM>,<NUM> of which corresponded to machine faults. The fault classifier thus trained may be termed the pre-existing fault classifier.

The pre-existing classifier was then modified in accordance with a preferred embodiment of the present invention, in order to be rendered capable of classifying faults based on signals acquired by tri-axial vibration sensors from rotating machines including bearings. The new data set of signals acquired by tri-axial vibration sensors and used to modify the pre-existing fault classifier comprised <NUM> signals sets, of which only <NUM> sets of signals were associated with faulty machine states, namely bearing wear. The pre-existing fault classifier was modified, as described above, based on this very small signal set. The modified fault classifier was then applied to a set of <NUM> signal sets, including <NUM> sets of signals corresponding to machine faults, acquired by tri-axial vibration sensors from rotating machines including bearings for fault detection, in order to test the performance thereof (line <NUM>). For the sake of comparison, an entirely new classifier was trained 'from scratch' with the same <NUM> sets of signals and also applied to the <NUM> example sets of data (line <NUM>). For the sake of completeness of comparison, the original pre-existing classifier in its unmodified form was also applied to the <NUM> example sets of data (line <NUM>). As is clear from consideration of <FIG>, the performance of the modified classifier in fault identification is the best, despite the small data set supplied. The scores listed in <FIG> are average precision scores, although other example scores may be used.

The pre-existing classifier was then modified, in order to be capable of classifying faults based on signals acquired by tri-axial vibration sensors from rotating machines including bearings. The new data set of signals acquired by tri-axial vibration sensors and used to modify the pre-existing fault classifier comprised <NUM> sets of signals, of which only <NUM> sets of signals were associated with faulty machine states, namely bearing wear. The pre-existing fault classifier was modified, as described above, based on this small signal set. The modified fault classifier was then applied to a set of <NUM> example sets of signals, including <NUM> sets of signals corresponding to machine faults, acquired by tri-axial vibration sensors from rotating machines including bearings for fault detection, in order to test the performance thereof (line <NUM>). For the sake of comparison, an entirely new classifier was trained with the same <NUM> sets of signals and also applied to the <NUM> example sets of data (line <NUM>). For the sake of completeness of comparison, the original pre-existing classifier in its unmodified form was also applied to the <NUM> example set of data (line <NUM>). As is clear from consideration of <FIG>, the performance of the modified classifier in fault identification is the best. The scores listed in <FIG> are average precision scores, although other example scores may be used.

In <FIG>, an example is shown of the performance, in the form of a precision-recall curve, of the modified classifier of the present invention. In this example, a fault classifier was trained and validated on a data set comprising over <NUM>,<NUM> labeled signal sets, of which <NUM>,<NUM> signal sets indicated machine faults, acquired from electrical motors including bearings, by single-axis vibration sensors. Following training, the fault classifier was tested on a data set comprising <NUM>,<NUM> signal sets, <NUM>,<NUM> of which corresponded to machine faults. The fault classifier thus trained may be termed the pre-existing fault classifier.

The pre-existing classifier was modified, in order to be capable of classifying faults based on magnetic flux signals acquired by magnetic sensors from electrical motors including bearings. The new data set of signals acquired by magnetic sensors and used to modify the pre-existing fault classifier comprised <NUM> sets of signals, of which only <NUM> sets of signals were associated with faulty machine states, namely bearing wear. The pre-existing fault classifier was modified, as described above, based on this small signal set. The pre-existing fault classifier was then applied to a test signal set of <NUM> signals, including only <NUM> example sets of signals corresponding to machine faults, acquired by magnetic sensors from electrical motors including bearings for fault detection, in order to test the performance thereof (line <NUM>). For the sake of comparison, an entirely new classifier was trained with the same <NUM> sets of magnetic signals and also applied to the same <NUM> example sets of magnetic data (line <NUM>). For the sake of completeness of comparison, the original pre-existing classifier in its unmodified form was also applied to the same <NUM> example set of magnetic data (line <NUM>). As is clear from consideration of <FIG>, the performance of the modified classifier in fault identification is the best. The scores listed in <FIG> are average precision scores, although other example scores may be used.

In the above examples of <FIG>, the data set based on which the classifier <NUM> was originally trained consisted of data sensed from more than <NUM>,<NUM> individual rotating machines, including primarily motors, pumps, fans, gear boxes, chillers and compressors. The bearing recordings, some of which were measured more than once, were recorded over a period of three years. Each bearing was measured along three axes by a single axis piezo-electric vibration sensor. In the case that the new data set comprised data from a tri-axial vibration sensor, this was a MEMS tri-axial vibration sensor.

As evident from the above data, the fault ratio in the dataset was approximately <NUM>%. The classifier was designed to detect a single bearing fault, such that even in cases where additional non-faulty machine bearings gave rise to signals exhibiting the signature of bearing wear, due to the vicinity of the non-faulty bearings to faulty bearings and due to acoustic wave propagation between the bearings, the classifier was capable of correctly classifying the non-faulty bearings as such. Labelling of the data was carried out by more than <NUM> human experts.

Reference is now made to <FIG>, which is a simplified flow chart illustrating steps involved in a method for mechanical machine fault identification based on transfer learning, in accordance with a preferred embodiment of the present invention.

Shown in <FIG> is a method <NUM> for machine fault identification. As seen at a first step <NUM>, a first set of signals emanating from a plurality of mechanical machines may be acquired by a first plurality of sensors. Sensors of the first plurality of sensors are preferably of a first, mutually same, type. Alternatively, sensors of the first plurality of sensors may be of multiple types. Sensors of the first plurality of sensors may have generally the same sensor frequency response, which may be termed the first sensor frequency response distribution.

As seen at a second step <NUM>, a first set of machine operational condition data may optionally be acquired, corresponding to the first set of signals acquired at step <NUM>. The machine operational condition data may include identification of an operational state associated with each set of signals of the first set of signals. The operational state may be a faulty or non-faulty state.

As seen at a third step <NUM>, the sets of signals and optional corresponding operational condition data are supplied to a pre-existing fault classifier, previously trained to identify faults in machines based on signals emanating from the machines and acquired by a second plurality of sensors. The second plurality of sensors, based on which the pre-existing fault classifier was trained, may be of a mutually same type as each other but different from the type of the first plurality of sensors. The second plurality of sensors, based on which the pre-existing fault classifier was trained, may alternatively be of multiple types, at least some of which are different from the first plurality of sensors. Sensors of the second plurality of sensors may have generally the same sensor frequency response, which may be termed the second sensor frequency response distribution. The second sensor frequency response distribution may be different from the first sensor frequency response distribution of the first plurality of sensors.

As seen at a fourth step <NUM>, the pre-existing fault classifier is preferably modified, using a transfer learning approach and based on the new data supplied thereto. The modification may involve the addition of at least one mapping layer to the pre-existing fault classifier, which mapping layer may learn the frequency response difference between the first sensor frequency response distribution and the second sensor frequency response distribution.

As seen at a fifth step <NUM>, an additional set of signals may subsequently be acquired from at least one machine by the same sensor type as the first plurality of sensors.

As seen at a sixth step <NUM>, the modified classifier produced at step <NUM> may be applied to the additional set of signals, in order to identify faults in the machines by which the additional set of signals were generated.

As seen at a seventh step <NUM>, based on the faults identified, machine maintenance or repair may be performed.

It is understood that all of the various machines described as generating signals at steps <NUM>, <NUM> and <NUM> may be the same machines or different machines having a shared characteristic, as described hereinabove.

Claim 1:
A method (<NUM>) for identifying a fault of at least one mechanical machine, comprising:
causing (<NUM>) a first plurality of sensors (<NUM>) coupled to a corresponding first plurality of mechanical machines (<NUM>) to acquire a first plurality of sets of signals emanating from said first plurality of mechanical machines, said first plurality of mechanical machines sharing at least one characteristic;
supplying (<NUM>) at least said first plurality of sets of signals of said first plurality of mechanical machines to a pre-existing fault classifier (<NUM>) comprising a neural network (<NUM>) previously trained to automatically identify faults of a second plurality of mechanical machines (<NUM>) based on signals emanating therefrom and previously acquired by a second plurality of sensors (<NUM>), said second plurality of sensors being of a different type than said first plurality of sensors, said second plurality of mechanical machines sharing said at least one characteristic;
modifying (<NUM>) said pre-existing fault classifier by employing transfer learning, based at least on said first plurality of sets of signals of said first plurality of mechanical machines, thereby providing a modified fault classifier (<NUM>), said modifying comprising adding at least one mapping layer (<NUM>) to said neural network and retraining the modified fault classifier, including said mapping layer, with said first plurality of sets of signals acquired by said first plurality of sensors;
applying (<NUM>) said modified fault classifier to at least one additional set of signals acquired by at least one sensor of said first plurality of sensors and emanating from at least one given mechanical machine sharing said at least one characteristic, said modified fault classifier being configured to automatically identify at least one fault of said at least one given mechanical machine based on said at least one additional set of signals; and
providing (<NUM>) a human sensible output, by an output device, including at least identification of said fault of said at least one given mechanical machine, at least one of a repair or maintenance operation being performed based on said human sensible output.