Patent Description:
An elevator system can include various sensors to detect the current state of system components and fault conditions. To perform certain types of fault or degradation detection, precise sensor system calibration may be needed. Sensor systems as manufactured and installed can have some degree of variation. Sensor system responses can vary compared to an ideal system due to these sensor system differences and installation differences, such as elevator component characteristic variations in weight, structural features, and other installation effects. <CIT> describes a method for designing a regulator for an elevator car through optimization of a model of the elevator including known parameters and poorly-known parameters. In <CIT>, actuators of an elevator system are used to provide a force signal to excite an elevator car, and the resulting frequency responses are measured and compared with the model parameters, which are updated based on the measured frequency responses. <CIT> describes a method for operating an environmental maintenance system in which the operation level of the system is varied, and the system response is measured in order to calibrate the system for future use.

According to a first aspect, a method of elevator sensor system calibration is provided according to claim <NUM>.

Some embodiments may include where performing analytics model calibration includes applying transfer learning to determine a transfer function based on the one or more response changes across a range of data points produced by the known excitation.

Some embodiments may include where a baseline designation of the trained model is shifted according to the transfer function.

Some embodiments may include where transfer learning shifts at least one fault detection boundary of the trained model.

Some embodiments may include where transfer learning shifts at least one trained regression model.

Some embodiments may include where transfer learning shifts at least one trained fault detection model, and a fault designation includes one or more of: a roller fault, a track fault, a sill fault, a door lock fault, a belt tension fault, a car door fault, and a hall door fault.

Some embodiments may include where one or more variations of the known excitation applied by the calibration device at one or more predetermined locations on an elevator system are collected.

Some embodiments may include where the known excitation includes a predetermined sequence of one or more vibration frequencies applied at one or more predetermined amplitudes.

Some embodiments may include where the data is collected at two or more different landings of an elevator system.

Technical effects of embodiments of the present disclosure include elevator sensor system calibration using injection of a known excitation and transfer learning to calibrate a trained model based on response changes between an actual response and an expected response to the known excitation to improve fault detection accuracy.

These <NUM> features and elements as well as the operation thereof will become more apparent in light of the following description and the accompanying drawings.

<FIG> is a perspective view of an elevator system <NUM> including an elevator car <NUM>, a counterweight <NUM>, one or more load bearing members <NUM>, a guide rail <NUM>, a machine <NUM>, a position encoder <NUM>, and an elevator controller <NUM>. The elevator car <NUM> and counterweight <NUM> are connected to each other by the load bearing members <NUM>. The load bearing members <NUM> may be, for example, ropes, steel cables, and/or coated-steel belts.

The load bearing members <NUM> engage the machine <NUM>, which is part of an overhead structure of the elevator system <NUM>. The position encoder <NUM> may be mounted on an upper sheave of a speed-governor system <NUM> and may be configured to provide position signals related to a position of the elevator car <NUM> within the elevator shaft <NUM>. In other embodiments, the position encoder <NUM> may be directly mounted to a moving component of the machine <NUM>, or may be located in other positions and/or configurations as known in the art.

The elevator controller <NUM> is located, as shown, in a controller room <NUM> of the elevator shaft <NUM> and is configured to control the operation of the elevator system <NUM>, and particularly the elevator car <NUM>. For example, the elevator controller <NUM> may provide drive signals to the machine <NUM> to control the acceleration, deceleration, leveling, stopping, etc. of the elevator car <NUM>. The elevator controller <NUM> may also be configured to receive position signals from the position encoder <NUM>. When moving up or down within the elevator shaft <NUM> along guide rail <NUM>, the elevator car <NUM> may stop at one or more landings <NUM> as controlled by the elevator controller <NUM>. Although shown in a controller room <NUM>, those of skill in the art will appreciate that the elevator controller <NUM> can be located and/or configured in other locations or positions within the elevator system <NUM>. In some embodiments, the elevator controller <NUM> can be configured to control features within the elevator car <NUM>, including, but not limited to, lighting, display screens, music, spoken audio words, etc..

The machine <NUM> may include a motor or similar driving mechanism and an optional braking system. Although shown and described with a rope-based load bearing system, elevator systems that employ other methods and mechanisms of moving an elevator car within an elevator shaft, such as hydraulics or any other methods, may employ embodiments of the present disclosure.

The elevator car <NUM> includes at least one elevator door assembly <NUM> operable to provide access between the each landing <NUM> and the interior (passenger portion) of the elevator car <NUM>. <FIG> depicts the elevator door assembly <NUM> in greater detail. In the example of <FIG>, the elevator door assembly <NUM> includes a door motion guidance track <NUM> on a header <NUM>, an elevator door <NUM> including multiple elevator door panels <NUM> in a center-open configuration, and a sill <NUM>. The elevator door panels <NUM> are hung on the door motion guidance track <NUM> by rollers <NUM> to guide horizontal motion in combination with a gib <NUM> in the sill <NUM>. Other configurations, such as a side-open door configuration, are contemplated. One or more sensors <NUM> are incorporated in the elevator door assembly <NUM> and are operable to monitor the elevator door <NUM>. For example, one or more sensors <NUM> can be mounted on or within the one or more elevator door panels <NUM> and/or on the header <NUM>. In some embodiments, motion of the elevator door panels <NUM> is controlled by an elevator door controller <NUM>, which can be in communication with the elevator controller <NUM> of <FIG>. In other embodiments, the functionality of the elevator door controller <NUM> is incorporated in the elevator controller <NUM> or elsewhere within the elevator system <NUM> of <FIG>. Further, calibration processing as described herein can be performed by any combination of the elevator controller <NUM>, elevator door controller <NUM>, a service tool <NUM> (e.g., a local processing resource), and/or cloud computing resources <NUM> (e.g., remote processing resources). The sensors <NUM> and one or more of: the elevator controller <NUM>, the elevator door controller <NUM>, the service tool <NUM>, and/or the cloud computing resources <NUM> can be collectively referred to as an elevator sensor system <NUM>.

The sensors <NUM> can be any type of motion, position, acoustic, or force sensor or acoustic sensor, such as an accelerometer, a velocity sensor, a position sensor, a force sensor, a microphone or other such sensors known in the art. The elevator door controller <NUM> can collect data from the sensors <NUM> for control and/or diagnostic/prognostic uses. For example, when embodied as accelerometers, acceleration data (e.g., indicative of vibrations) from the sensors <NUM> can be analyzed for spectral content indicative of an impact event, component degradation, or a failure condition. Data gathered from different physical locations of the sensors <NUM> can be used to further isolate a physical location of a degradation condition or fault depending, for example, on the distribution of energy detected by each of the sensors <NUM>. In some embodiments, disturbances associated with the door motion guidance track <NUM> can be manifested as vibrations on a horizontal axis (e.g., direction of door travel when opening and closing) and/or on a vertical axis (e.g., up and down motion of rollers <NUM> bouncing on the door motion guidance track <NUM>). Disturbances associated with the sill <NUM> can be manifested as vibrations on the horizontal axis and/or on a depth axis (e.g., in and out movement between the interior of the elevator car <NUM> and an adjacent landing <NUM>.

Embodiments are not limited to elevator door systems but can include any elevator sensor system within the elevator system <NUM> of <FIG>. For example, sensors <NUM> can be used in one or more elevator subsystems for monitoring elevator motion, door motion, position referencing, leveling, environmental conditions, and/or other detectable conditions of the elevator system <NUM>.

To support calibration of the elevator sensor system <NUM>, a calibration device <NUM> can be placed in contact with the elevator door <NUM> at one or more predetermined locations <NUM> to apply a known excitation that is detectable by the sensors <NUM>. The calibration device <NUM> can be configured to inject a predetermined sequence of one or more vibration frequencies applied at one or more predetermined amplitudes to one or more of the predetermined locations <NUM>. For instance, placing the calibration device <NUM> closer to the door motion guidance track <NUM> can induce a vibration more similar to a roller fault or a track fault, while placing the calibration device <NUM> closer to the sill can induce a vibration more similar to a sill fault. The calibration device <NUM> need not precisely simulate an actual fault, as the actual sensed response to the excitation can be used to calibrate a trained model as further described herein.

<FIG> depicts a transfer learning process <NUM> according to an embodiment. At an experiment site <NUM>, a known excitation <NUM> provides a known calibration signal to an instance of the elevator sensor system <NUM> of <FIG>. Data <NUM> is collected by instances of the sensors <NUM> of <FIG> at the experiment site <NUM> responsive to the known excitation <NUM>. A response to the known excitation <NUM> for a non-faulty configuration at the experiment site <NUM> can be determined relative to a feature space <NUM> of a trained model that establishes a baseline designation <NUM>, a fault designation <NUM>, and one or more fault detection boundaries <NUM>.

Multiple experiments can be run at the experiment site <NUM> to establish the feature space <NUM> used to detect and classify various features. For example, the baseline designation <NUM> in the feature space <NUM> can establish a nominal expected response to cycling of the elevator door <NUM> of <FIG> in a horizontal motion between an open and closed position and/or between a closed and open position. The baseline designation <NUM> may represent expected frequency response characteristics of an instance of the elevator door assembly <NUM> of <FIG> at the experiment site <NUM> for a non-faulty configuration. The one or more fault detection boundaries <NUM> can be used to establish boundaries or regions within the feature space <NUM> of a likelihood of a fault/no-fault condition and/or for trending to observe response shifts headed from the baseline designation <NUM> towards the fault designation <NUM>, e.g., a progressive degraded response. The experiment site <NUM> can be a test lab or a field location known to have one or more components in a faulty/degraded condition. For instance, the experiment site <NUM> in a lab or field location can have known correctly working components and known worn/broken components to use for baseline development and model training.

Observations can be made at the experiment site <NUM> as to the effect of applying the known excitation <NUM> at one or more predetermined locations <NUM> of <FIG> using one or more vibration profiles, such as a sinusoidal sweep of vibration frequencies at a fixed or varying amplitude while the elevator doors <NUM> remain in a substantially fixed position (e.g., closed). An expected response to the known excitation <NUM> can be quantified in the form of resulting offsets in the feature space <NUM> from the baseline designation <NUM>, fault designation <NUM>, and/or fault detection boundaries <NUM>, for instance, in multiple dimensions.

To calibrate instances of the elevator sensor system <NUM> of <FIG> at one or more field sites <NUM>, a known excitation <NUM> that is equivalent to the known excitation <NUM> provides a known calibration signal to the elevator sensor system <NUM> using the calibration device <NUM>. At each of the field sites <NUM>, data <NUM> is collected by instances of the sensors <NUM> of <FIG> responsive to the known excitation <NUM>. An expected response from the experiment site <NUM> is transferred <NUM> to the field sites <NUM> for comparison with an actual response to the known excitation <NUM>. Various transfer learning algorithms, such as baseline relative feature extraction, baseline affine mean shifting, similarity-based feature transfer, covariate shifting by kernel mean matching, and/or other transfer learning techniques known in the art, can be used to develop a transfer function <NUM> with respect to feature spaces <NUM>, <NUM>. The known excitation <NUM> can provide a range of data points beyond baseline designation <NUM>. For example, the known excitation <NUM> can expose non-linearity which can be accounted for in the transfer function <NUM> to improve model accuracy. The feature space <NUM> at the field sites <NUM> can initially be equivalent to a copy of the feature space <NUM> of a trained model that establishes a baseline designation <NUM> equivalent to baseline designation <NUM>, a fault designation <NUM> equivalent to fault designation <NUM>, and one or more fault detection boundaries <NUM> equivalent to fault detection boundaries <NUM>. The transfer function <NUM> can be generated using transfer learning from baseline data collection (baseline designation <NUM>, <NUM>), sensed calibrated signal data of known excitation <NUM>, and a response collected in data <NUM>. The result of applying transfer function <NUM> to models in feature space <NUM> is that the fault data signature <NUM> and detection boundary <NUM> are calibrated according to the specific waveform propagation characteristics of the field site <NUM>. The calibrated fault detection boundary <NUM> and calibrated fault designation <NUM> (i.e., data signature) represent a calibrated analytics model.

In embodiments, transfer learning can be used for trained model calibration at field sites <NUM> based on known excitation <NUM> applied at one or more predetermined locations <NUM> of <FIG> using the calibration device <NUM> to apply one or more vibration profiles, such as a sinusoidal sweep of vibration frequencies at a fixed or varying amplitude while the elevator doors <NUM> of <FIG> remain in a substantially fixed position (e.g., closed). Differences between the expected response at the experiment site <NUM> and the actual response at field sites <NUM> are quantified to produce calibrated feature shifts in feature space <NUM> as transfer function <NUM>. For example, baseline designation <NUM> can be shifted to account for response changes as a calibrated baseline designation <NUM>. Similarly, fault designation <NUM> can be shifted to account for response changes as a calibrated fault designation <NUM>. Further, one or more fault detection boundaries <NUM> can be shifted to account for response changes as one or more calibrated fault detection boundaries <NUM>. The shifting in feature space <NUM> can translate into adjustments of various trained models for feature detection, classification, and regression, for example, as further described with respect to <FIG>.

<FIG> depicts an analytics model calibration process <NUM> according to an embodiment. At one of the field sites <NUM> of <FIG>, a computing system of the elevator sensor system <NUM> of <FIG> can receive actual sensor input <NUM> from one or more sensors <NUM> of <FIG>. The actual sensor input <NUM> in response to the known excitation <NUM> of <FIG> can be provided to a trained model <NUM> received from the experiment site <NUM> of <FIG>. An expected response <NUM> to the known excitation <NUM> (e.g., based on previous experiments at the experiment site <NUM>) and an actual response <NUM> to the known excitation <NUM> can be analyzed by analytics model calibration <NUM> to perform transfer learning. The analytics model calibration <NUM> can apply transfer learning to determine the transfer function <NUM> of <FIG> to calibrate the trained model <NUM> based on one or more response changes determined between the actual response <NUM> and the expected response <NUM>. Multiple transfer learning algorithms are contemplated. For example, transfer learning performed by analytics model calibration <NUM> can apply baseline relative feature extraction, baseline affine mean shifting, similarity-based feature transfer, covariate shifting by kernel mean matching, and/or other transfer learning techniques known in the art. Transfer learning performed in the analytics model calibration <NUM> can shift a fault designation <NUM> of the trained model <NUM> as calibrated fault designation <NUM>, and/or shifts at least one fault detection boundary <NUM> of the trained model <NUM> as calibrated fault detection boundary <NUM> of <FIG>.

The shifting within trained model <NUM> based on the transfer function <NUM> of <FIG> can result in changes to feature definitions <NUM> used by a detection process <NUM>, changes to a trained classification model <NUM> used by a classification process <NUM>, and/or changes to a trained regression model <NUM> used by a regression process <NUM>. For example, once calibration of the trained model <NUM> is performed, the actual sensor input <NUM> can be provided to signal conditioning <NUM> as part of a condition determination process <NUM>. The signal conditioning <NUM> can include filtering, offset corrections, and/or time/frequency domain transforms, such as applying wavelet transforms to produce a spectrum of feature data. The feature definitions <NUM> (e.g., defined with respect to the feature space <NUM> of <FIG>) can be used by the detection process <NUM> to detect potentially useful features from spectral data of the signal conditioning <NUM>. For instance, the detection process <NUM> may search for higher energy responses within targeted frequency ranges. The trained classification model <NUM> can be used by the classification process <NUM> to classify detected features from the detection process <NUM>, e.g., identifying detected features as fault designations along with specific fault types such as a roller fault, a track fault, a sill fault, and the like. The regression process <NUM> can use the trained regression model <NUM> to determine the strength/weakness of various classifications to support trending, prognostics, diagnostics, and the like based on classifications from the classification process <NUM>.

Referring now to <FIG>, an exemplary computing system <NUM> that can be incorporated into elevator systems of the present disclosure is shown. The computing system <NUM> may be configured as part of and/or in communication with an elevator controller, e.g., controller <NUM> shown in <FIG>, and/or as part of the elevator door controller <NUM>, service tool <NUM>, and/or cloud computing resources <NUM> of <FIG> as described herein. When implemented as service tool <NUM>, the computing system <NUM> can be a mobile device, tablet, laptop computer, or the like. When implemented as cloud computing resources <NUM>, the computing system <NUM> can be located at or distributed between one or more network-accessible servers. The computing system <NUM> includes a memory <NUM> which can store executable instructions and/or data associated with control and/or diagnostic/prognostic systems of the elevator door <NUM> of <FIG>. The executable instructions can be stored or organized in any manner and at any level of abstraction, such as in connection with one or more applications, processes, routines, procedures, methods, etc. As an example, at least a portion of the instructions are shown in <FIG> as being associated with a control program <NUM>.

Further, as noted, the memory <NUM> may store data <NUM>. The data <NUM> may include, but is not limited to, elevator car data, elevator modes of operation, commands, or any other type(s) of data as will be appreciated by those of skill in the art. The instructions stored in the memory <NUM> may be executed by one or more processors, such as a processor <NUM>. The processor <NUM> may be operative on the data <NUM>.

The processor <NUM>, as shown, is coupled to one or more input/output (I/O) devices <NUM>. In some embodiments, the I/O device(s) <NUM> may include one or more of a keyboard or keypad, a touchscreen or touch panel, a display screen, a microphone, a speaker, a mouse, a button, a remote control, a joystick, a printer, a telephone or mobile device (e.g., a smartphone), a sensor, etc. The I/O device(s) <NUM>, in some embodiments, include communication components, such as broadband or wireless communication elements.

The components of the computing system <NUM> may be operably and/or communicably connected by one or more buses. The computing system <NUM> may further include other features or components as known in the art. For example, the computing system <NUM> may include one or more transceivers and/or devices configured to transmit and/or receive information or data from sources external to the computing system <NUM> (e.g., part of the I/O devices <NUM>). For example, in some embodiments, the computing system <NUM> may be configured to receive information over a network (wired or wireless) or through a cable or wireless connection with one or more devices remote from the computing system <NUM> (e.g. direct connection to an elevator machine, etc.). The information received over the communication network can stored in the memory <NUM> (e.g., as data <NUM>) and/or may be processed and/or employed by one or more programs or applications (e.g., program <NUM>) and/or the processor <NUM>.

The computing system <NUM> is one example of a computing system, controller, and/or control system that is used to execute and/or perform embodiments and/or processes described herein. For example, the computing system <NUM>, when configured as part of an elevator control system, is used to receive commands and/or instructions and is configured to control operation of an elevator car through control of an elevator machine. For example, the computing system <NUM> can be integrated into or separate from (but in communication therewith) an elevator controller and/or elevator machine and operate as a portion of elevator sensor system <NUM> of <FIG>.

The computing system <NUM> is configured to operate and/or control calibration of the elevator sensor system <NUM> of <FIG> using, for example, a flow process <NUM> of <FIG>. The flow process <NUM> can be performed by a computing system <NUM> of the elevator sensor system <NUM> of <FIG> as shown and described herein and/or by variations thereon. Various aspects of the flow process <NUM> can be carried out using one or more sensors, one or more processors, and/or one or more machines and/or controllers. For example, some aspects of the flow process involve sensors, as described above, in communication with a processor or other control device and transmit detection information thereto. The flow process <NUM> is described in reference to <FIG>.

At block <NUM>, a computing system <NUM> collects a plurality of data from one or more sensors <NUM> of an elevator sensor system <NUM> while a calibration device <NUM> applies a known excitation <NUM>, for instance, to an elevator door <NUM>. In some embodiments, one or more variations of the known excitation <NUM> are applied by the calibration device <NUM> at one or more predetermined locations <NUM> on the elevator door <NUM>. The known excitation <NUM> can include a predetermined sequence of one or more vibration frequencies applied at one or more predetermined amplitudes. The data can be collected at two or more different landings <NUM> of elevator system <NUM>, e.g., to perform floor-level specific calibration of the elevator sensor system <NUM>.

At block <NUM>, the computing system <NUM> compares an actual response <NUM> to an expected response <NUM> to the known excitation <NUM> using a trained model <NUM>. The trained model <NUM> is trained by applying a known excitation <NUM> to a different instance of the elevator sensor system <NUM> at experiment site <NUM> to produce the expected response <NUM>, which can be reproduced at field sites <NUM>.

At block <NUM>, the computing system <NUM> performs analytics model calibration <NUM> to calibrate the trained model <NUM> based on one or more response changes between the actual response <NUM> and the expected response <NUM>. Transfer learning can be applied to determine a transfer function <NUM> based on the one or more response changes across a range of data points produced by the known excitation <NUM>.

As described herein, in some embodiments various functions or acts may take place at a given location and/or in connection with the operation of one or more apparatuses, systems, or devices. For example, in some embodiments, a portion of a given function or act may be performed at a first device or location, and the remainder of the function or act may be performed at one or more additional devices or locations.

Embodiments may be implemented using one or more technologies. In some embodiments, an apparatus or system may include one or more processors and memory storing instructions that, when executed by the one or more processors, cause the apparatus or system to perform one or more methodological acts as described herein. Various mechanical components known to those of skill in the art may be used in some embodiments.

Embodiments may be implemented as one or more apparatuses, systems, and/or methods. In some embodiments, instructions may be stored on one or more computer program products or computer-readable media, such as a transitory and/or non-transitory computer-readable medium. The instructions, when executed, may cause an entity (e.g., an apparatus or system) to perform one or more methodological acts as described herein.

Claim 1:
A method comprising:
collecting, by a computing system (<NUM>), a plurality of data from one or more sensors (<NUM>) of an elevator sensor system (<NUM>) while a calibration device (<NUM>) applies a known excitation (<NUM>, <NUM>);
characterized by:
comparing, by the computing system (<NUM>), an actual response (<NUM>) to an expected response (<NUM>) to the known excitation (<NUM>, <NUM>) using a trained model (<NUM>), wherein the trained model (<NUM>) is trained by applying the known excitation (<NUM>, <NUM>) to a different instance of the elevator sensor system (<NUM>) to produce the expected response (<NUM>);
and
performing, by the computing system (<NUM>), analytics model calibration (<NUM>) to calibrate the trained model (<NUM>) based on one or more response changes between the actual response (<NUM>) and the expected response (<NUM>).