Predictive analysis system and method for analyzing and detecting machine sensor failures

A system for performing predictive analysis and diagnostics is disclosed. The system includes a plurality of sensors communicatively coupled to a vehicle electronics unit. The plurality of sensors are configured to generate at least one first signal indicative of a first sensed condition and at least one second signal indicative of a second sensed condition. A remote central processing system is coupled to the vehicle electronics unit. The remote central processing system comprises a remote processor and a remote data storage device, wherein the remote central processing system is configured to receive each of the at least one first and second signals. A predictive diagnostic unit is arranged in the remote data storage device and comprises machine readable instructions that, when executed by the remote processor, causes the system to partition the second signal into a predetermined number of successive time intervals; generate a similarity value based on a comparative analysis between the partitioned second signal and a stored first signal; and determine an estimated degree of failure of a machine component based in part on a computed average of the similarity value.

TECHNICAL FIELD

The present generally relates to a predictive analysis system and method for analyzing and detecting vehicle sensor failures.

BACKGROUND

During machine operations, the ability to detect or predict machine failure is essential in critical applications such as trucking, excavation, marine, construction, forestry and agricultural applications, and others or reduce machine downtime. To address such concerns various failure detection and diagnosis methods have been employed.

For example, many conventional approaches utilize the following three well known detection methods: knowledge-based detection, model-based detection, and signal-based detection, each of which applies a different detection approach. With knowledge-based detection, sensor readings are classified into time series and labeled to allow the information to be correlated for further detection. A drawback to such an approach includes the inability to precisely detect individual sensor failures, as well as decreased sensor bandwidth. Model-based detection, on the other hand, includes generating virtual sensors in the form of correlated “models,” which are compared with machine performance during machine operation. Similar to knowledge-based detection, drawbacks include decreased sensor bandwidths, as well as increased correlation processes. Further, although signal-based detection is a widely used detection method which focuses on frequency-domain analysis, it is also limited in its ability to accurately capture sensor failures.

As such, to overcome the limitations and drawbacks associated with the prior art, there is a need in the art for a new and improved detection method that provides increased sensor bandwidth, as well as more precise data analyses.

SUMMARY

In accordance with one embodiment, a system for performing predictive analysis and diagnostics is disclosed. The system comprises a plurality of sensors communicatively coupled to a vehicle electronics unit. Each of the plurality of sensors are configured to generate at least one first signal indicative of a first sensed condition and at least one second signal indicative of a second sensed condition. A remote central processing system is coupled to the vehicle electronics unit. The remote central processing system comprises a remote processor and a remote data storage device, wherein the remote central processing system is configured to receive each of the at least one first and second signals. A predictive diagnostic unit is arranged in the remote data storage device and comprises machine readable instructions that, when executed by the remote processor, causes the system to partition the second signal into a predetermined number of successive time intervals; generate a similarity value based on a comparative analysis between the partitioned second signal and a stored first signal; and determine an estimated degree of failure of a machine component based in part on a computed average of the similarity value.

Like reference numerals are used to indicate like elements throughout the several figures.

DETAILED DESCRIPTION

Referring toFIG. 1, a predictive analysis system for predicting and/or diagnosing machine sensor failures is shown according to one embodiment. As will be discussed herein, the predictive analysis system100can be used to predict or diagnose failures of one or more sensors or other electrical components arranged on an agricultural vehicle or implement attached thereto. In some embodiments, the predictive analysis system100can comprise a central electronic processing system102communicatively coupled to a vehicle electronics unit104via a network106and wireless infrastructure108.

The central electronic processing system102can comprise a remote data processor122, a remote data storage device124, and a remote communications interface128coupled to a remote data bus126and may be implemented by a general-purpose computer or a server that is programmed with software modules stored in the remote data storage device124. The remote data processor122may comprise a microprocessor, a microcontroller, a central processing unit, a programmable logic array, an application specific integrated circuit (ASIC), a logic circuit, an arithmetic logic unit, or another data processing system for processing, storing, retrieving, or manipulating electronic data.

The remote data storage device124comprises electronic memory, nonvolatile random access memory, an optical storage device, a magnetic storage device, or another device for storing and accessing electronic data on any recordable, rewritable, or readable electronic, optical, or magnetic storage medium. For example, the remote data storage device124can store one or more of the following software modules, data structures or files: a diagnostic unit125and a diagnostic database127as will be discussed in further detail with reference toFIGS. 2A and 2B.

The remote communications interface128may comprise a transceiver or other device for communicating, transmitting, or receiving data via the communications network106. In one embodiment, the communications network106can comprise the Internet, the public switched telephone network (PSTN) or another public, or private electronic communications network106, or a communications link (e.g., telecommunications line or microwave link) that supports communication to or from the wireless infrastructure108.

The wireless infrastructure108supports wireless communications between the vehicle electronics104and the central electronic processing system102. The wireless infrastructure108may comprise one or more of the following: one more wireless base stations that are capable of communicating over the communications network106via a gateway an Internet service provider, or otherwise; one or more satellite transceivers; a satellite downlink receiver, a satellite uplink transmitter; a satellite communications system; a cellular infrastructure network; a trunking system, a point-to-multipoint communications system, a point-to-point communications link, a land-based wireless communications network, or the like.

The vehicle electronics unit104can comprise a vehicle data processor154, a vehicle data storage device156, a vehicle wireless communications device158, a user interface160, and a vehicle data bus162each communicatively interfaced with a main data bus164. As depicted, the various devices (i.e., vehicle data storage device156, vehicle wireless communications device158, user interface160, and vehicle data bus162) may communicate information, e.g., sensor signals, over the main data bus164to the vehicle data processor154. A local controller166may be coupled to the vehicle data bus162and can be configured to receive and process a plurality of sensor data signals from each of sensors168.

The vehicle data processor154manages the transfer of data to and from the central electronic processing system102via the communications network106and wireless infrastructure108. For example, the vehicle data processor154collects and processes data (e.g., sensor data representing one or more conditions or characteristics) from the main data bus164for transmission either in a forward or rearward direction (i.e., to or from processing system102). In various embodiments, the vehicle data processor154may comprise a microprocessor, a microcontroller, a central processing unit, a programmable logic array, an application specific integrated circuit, a logic circuit, an arithmetic logic unit, or another data processing system for processing, storing, retrieving, or manipulating electronic data.

The vehicle data storage device156stores information and data for access by the vehicle data processor154or the vehicle data bus162. The vehicle data storage device156may comprise electronic memory, nonvolatile random access memory, an optical storage device, a magnetic storage device, or another device for storing and accessing electronic data on any recordable, rewritable, or readable electronic, optical, or magnetic storage medium. For example, the vehicle data storage device156may include one or more software modules that records and stores data collected by sensors168or other network devices coupled to or capable of communicating with the vehicle data bus162, or another sensor or measurement device for sending or measuring parameters, conditions or status of the vehicle electronics unit104, vehicle systems, or vehicle components. Sensors168may include various sensors such as, e.g., steering sensors, brake sensors, location sensors, pressure sensors, position sensors or other suitable sensing devices capable of generating system diagnostic data.

Referring now toFIGS. 2A and 2B, a block diagram of the diagnostic unit125discussed with reference toFIG. 1is shown according an embodiment. In some embodiments, the diagnostic unit125can comprise a data acquisition module202, a comparison module206and a failure analysis module208in communication with the diagnostic database127. The term module as used herein may include a hardware and/or software system that operates to perform one or more functions. Each module can be realized in a variety of suitable configurations, and should not be limited to any particular implementation exemplified herein, unless such limitations are expressly called out. Moreover, in the various embodiments described herein, each module corresponds to a defined functionality; however, it should be understood that in other contemplated embodiments, each functionality may be distributed to more than one module. Likewise, in other embodiments, multiple defined functionalities may be implemented by a single module that performs those multiple functions, possibly alongside other functions, or distributed differently among a set of modules than specifically illustrated in the examples herein.

The data acquisition module202receives a plurality of data signals (i.e., baseline and failure analysis signals) transmitted from each of the sensors168coupled to the vehicle data bus162via network106. In some embodiments, the data acquisition module202may comprise a partitioning unit212communicatively coupled to a vectorization unit214for sampling and digitizing the received signals. The partitioning unit212can comprise a sample and hold circuit232(FIG. 2B) that is configured to sample and hold the plurality data signals received by the data acquisition module202. For example, the received signals may be sampled and held at a plurality of points during a predetermined sampling period by the sample and hold circuit232. The sampling periods may be successive or dispersed time windows, each of which may be correlated to a fuel injection period of the vehicle engine. In other embodiments, the partitioning unit212may further comprise buffers, filters, or other suitable signal processing elements to provide increased signal quality of the received data signals.

The output of the partitioning unit212is fed into the vectorization unit214, which, in some embodiments may comprise an A/D converter234to convert each of the held signals (e.g., baseline and failure analysis signals) into digital sample to generate a series of vectors.

The comparison module206compares at least two vectors generated by the vectorization unit214from each of the baseline and failure analysis signals to determine a similarity value, and generates a similarity matrix to store the determined similarity values. The comparison module206is additionally configured to compute a weight ratio that is applied to each of the similarity values to account for instances in which the two vectors are equally dimensioned but exhibit different waveform characteristics (refer, e.g., toFIGS. 6A and 6B).

The failure analysis module208receives the similarity value and weight ratio generated by the comparison module206and determines a quantified degree of failure, which is a numerical value indicative of the amount of deviation between the baseline and failure analysis signals. For example, the failure analysis module208predicts a degree of failure of the various machine components, such as sensors168, based on a comparative analysis of the baseline and failure analysis signals. In some embodiments, the failure analysis module208may comprise a classifier unit216that is configured to classify groups of data related to anticipated events (e.g., healthy or unhealthy signal patterns that are characterized based on previous signal measurements).

Referring now toFIG. 3, a flow diagram of a method300for performing predictive analysis diagnostics is shown according to an embodiment. At step302, prior to operation, at least one baseline signal is measured by one or more of the plurality of sensors168once steady state conditions are reached during the vehicle start up. For example, in response to a triggering event, such as activation of a fuel injector via a control signal, the data acquisition module202receives and samples the baseline signals transmitted over the communications network106from the vehicle data bus162via vehicle data processor154.

In other embodiments, the baseline data may be obtained and stored during manufacturing. Once received, the data acquisition module202captures the baseline signals at predetermined time intervals, which may, e.g., be correlated with a fuel injection period of an engine, and stores the digitized samples (i.e., vectorized representations) of the captured signals in diagnostic database127of the remote processor122. For example, the baseline signals may be captured during successive time intervals such that successive groups of signals (e.g., baseline signal1, baseline signal2, and baseline signal3) are captured and stored in diagnostic database127. Additionally, because the baseline signals are captured under steady state conditions, each of the signals should exhibit similar waveform characteristics.

In operation, and at304, a plurality of failure analysis signals are measured and transmitted to the central processing system102via the communications network106. The plurality of failure analysis signals are received and sampled by the partitioning unit212of the data acquisition module202. Each partitioned segment of the failure analysis signals may be obtained by extracting a number of samples during various time intervals which may be of the same or different time durations (refer, e.g., toFIG. 4). Similar to the baseline signal, in some embodiments, measurement of the failure analysis signal can be triggered in response to a fuel injection system activation. In other embodiments, measurement of the failure analysis signal may be triggered in response to an operator input, which may in turn cause a phase delay in the received signal. As illustrated inFIG. 4, each sampling time window (e.g., T1-T4) can be of the same duration as that of the baseline signal to ensure computation accuracy. Each time window includes a plurality of vectors representative of phase and amplitude patterns.

Next at306, the comparison module206compares each sampled segment of the failure analysis signal against the stored baseline signals (e.g., baseline signal1-3) as discussed with reference toFIG. 4. First, an angle between at least two vectors generated by the vectorization unit214is determined utilizing an algorithm (e.g., a vector space algorithm) implemented on the comparison module206. In particular, a cosine similarity between the at least two vectors of each of the baseline and failure analysis signals is computed to identify a signal failure pattern. The cosine similarity function is determined according to the following equation:

sim⁢⁢θ⁡(d,q)=d·qd×q,(1)
where d and q are vectorized representations of each of the baseline and failure analysis signals, and simθ(d, q) is a cosine of the angles between vectors d and q. In other words, d and q represent a plurality of data points (i.e., vectors) within each time interval.

For example, at 0 degrees, vectors d and q will have a maximum degree of similarity that corresponds to a measured cosine value of 1, whereas at 180 degrees, the vectors d and q will have a minimum degree of similarity that corresponds to a measured cosine value of −1. A maximum degree of similarity indicates that the baseline and failure analysis signals exhibit substantially similar characteristics and a minimum degree of similarity indicates that the signals have distinguishing characteristics. At308, a similarity matrix will be generated by the comparison module206. Each similarity matrix will include a row value and a column value which are determined based on the baseline and failure analysis signals. For example, the row value will be determined based on the number of sampling segments (e.g., n=4) in which the failure analysis signal is divided into, and the column value will be determined based on the number of similarity values computed for each baseline signal (e.g., m=3).

Once the similarity value is determined, a weight ratio α is computed at310and is used to account for instances in which the dimensions of d and q are equal but the measured signals, in fact, have differing characteristics (refer, e.g., toFIGS. 6A and 6B). The weight ratio α is calculated based on the root mean square (RMS) according to the following equation:

α=RMS⁡(Baseline)RMS⁡(Failure⁢⁢Analysis),(3)
where the RMS value is determined in part based on the value of the integral from a time t=a to t=b utilizing the equation below:

RMS⁢⁢Value=1b-a⁢∫ab⁢y2⁢dt.(2)
Once the weight ratio is determined, at312, a decision is made as to whether or not the computed weight ratio will be used. For example, if α (i.e., the weight ratio) is larger than 1, the inverse value of a is used in determining a quantified degree of failure.

At314, the quantified degree of failure (DOF) is computed by the failure analysis module208according to the following equation:
DOF=(simθ(d,q))*(α)  (4).
A Return DOF value is then computed at316which is a summed average (i.e., mean) of the DOF values computed for each sample of the failure analysis signal (i.e., Return DOF=avg (DOF1, DOF2, DOF3, DOF4, . . . DOFn)) to reduce computation errors. Lastly at318, the DOF is compared against a threshold value, which can be determined by user experience or based on a recommended value developed through a previous database. For example, a Return DOF value above the threshold indicates a healthy signal condition, whereas a Return DOF value below threshold indicates a failure has occurred or is near occurrence. In response to a failure indication, a warning signal will be given, which may be displayed on user interface160of the vehicle electronics104for view by an operator.

Referring toFIG. 5, a schematic illustration of a gas injection system500utilizing the predictive analysis system100of the present disclosure is shown. The gas injection system500can comprise a pressure regulator503coupled to a plurality of gas injectors504via a manifold line502. In the example embodiment, if a pressure leak is experienced on the manifold line502, a pressure drop will occur, thereby causing changes in the output signals generated by sensors168discussed with reference toFIG. 1. InFIGS. 6A and 6B, exemplary baseline and failure analysis signal traces602,604are shown. In bothFIGS. 6A and 6B, each DOF value was computed utilizing 100 samples per interval of the baseline and failure analysis signals. InFIG. 6A, for example, each signal trace was measured in the absence of noise and a quantified degree of failure (DOF) value of approximately 0.9715 was computed.

InFIG. 6B, each signal612and614was measured with a noise level of around 1/10 of the original pressure signal and a quantified degree of failure (DOF) value of approximately 0.9874 was computed. As discussed with reference toFIG. 3, due to the similarity of the traces in bothFIGS. 6A and 6B, the weight ratio should be computed to account for the distinguishing characteristics of each curve, which would not be captured by individual computation of the similarity value (simθ(d, q)).

Without in any way limiting the scope, interpretation, or application of the claims appearing below, a technical effect of one or more of the example embodiments disclosed herein is a predictive analysis system and method for analyzing and detecting vehicle sensor failures. While the present disclosure has been illustrated and described in detail in the drawings and foregoing description, such illustration and description is not restrictive in character, it being understood that illustrative embodiment(s) have been shown and described and that all changes and modifications that come within the spirit of the present disclosure are desired to be protected. Alternative embodiments of the present disclosure may not include all of the features described yet still benefit from at least some of the advantages of such features. Those of ordinary skill in the art may devise their own implementations that incorporate one or more of the features of the present disclosure and fall within the spirit and scope of the appended claims.