Vehicle fault root cause diagnosis

A method of root cause diagnosis of fault data from a vehicle includes identifying a first vehicle fault and selecting from field repair data a vehicle feature corresponding to the identified first vehicle fault. The method also includes identifying from the field repair data an effective repair of the identified first vehicle fault. The method additionally includes training and testing via a machine learning algorithm, a labor code classifier using the identified effective repair of the first vehicle fault and the selected vehicle feature corresponding to the identified first vehicle fault. The method also includes identifying and classifying, using the trained classifier, indistinguishable labor codes. Furthermore, the method includes communicating the identified and classified indistinguishable labor codes for diagnosing a root cause of real time first vehicle fault data. A computer-readable medium storing an executable computer algorithm for performing the root cause diagnosis of vehicle fault data is also envisioned.

INTRODUCTION

The present disclosure relates to root cause diagnosis of vehicle system faults.

Vehicles may experience various concerns, issues, or faults during their operation. Serious vehicle faults may cause the vehicle to become immobile, but, generally, the majority of faults in a vehicle lead to user dissatisfaction. A vehicle breakdown is typically either an electrical or a mechanical failure in which the underlying fault prevents the vehicle from being operated at all, or makes the vehicle difficult to operate. Depending on the nature and severity of the fault, a vehicle may or may not need to be towed to a repair shop, such as an authorized dealership.

A breakdown occurs when a vehicle stalls on the road. A vehicle may stall for a variety of faults ranging from a dead battery, fuel pump, poor quality fuel, faulty electrical wiring or sensors, fuel pressure problems, overlooked leaks, etc. A complete vehicle breakdown takes place when the vehicle becomes totally immobile and may not be driven even a short distance to reach a repair shop, thereby necessitating a tow. A complete breakdown may occur for a variety of reasons, including engine or transmission failure, or a dead starter or battery, though a dead battery may be able to be temporarily resolved with a jump start.

In a partial breakdown, the vehicle may still be operable, but its operation may become more limited or its continued operation may contribute to further vehicle damage. Often, when a partial breakdown occurs, it may be possible to drive the vehicle to a repair shop, thereby avoiding a tow. Some common causes of a partial breakdown include overheating, brake failure, and intermittent stalling. Some faults do not lead to vehicle breakdowns, but may, for example, impede full use of the vehicle's infotainment or climate control systems. Some of the above vehicle faults may be intermittent—they set a diagnostic trouble code, but then recover by themselves. Such faults may be difficult to diagnose or duplicate, and may cause vehicle componentry to be replaced without resolving the issue. Generally, intermittent vehicle faults tend to increase warranty costs and may also negatively impact customer satisfaction.

SUMMARY

A method of root cause diagnosis of fault data from a vehicle includes identifying a first vehicle fault and selecting from field repair data, via an executable computer algorithm, a vehicle feature corresponding to the identified first vehicle fault. The method also includes identifying from the field repair data, via the executable computer algorithm, an effective repair of the identified first vehicle fault. The method additionally includes training and testing via a machine learning algorithm, a labor code classifier using the identified effective repair of the first vehicle fault and the selected vehicle feature corresponding to the identified first vehicle fault. The method also includes identifying and classifying, via the executable computer algorithm, using the trained labor code classifier, indistinguishable, e.g., ambiguous by test result, labor codes. Furthermore, the method includes communicating the identified and classified indistinguishable labor codes for diagnosing a root cause of real time first vehicle fault data. The method may be specifically used to diagnose intermittent system faults.

The act of selecting the vehicle feature from field repair data may include selecting the field repair data from a vehicle fleet.

The act of selecting a vehicle feature corresponding to the identified first vehicle fault includes selecting the vehicle feature from a predefined set of vehicle features.

The act of selecting the vehicle feature from a predefined set of vehicle features may include identifying a second vehicle fault that is unrelated to the first vehicle fault, i.e., has a known different root cause. The act of selecting the vehicle feature from a predefined set of vehicle features may also include comparing probability distributions of the vehicle features from the predefined set of vehicle features for the first vehicle fault and for the second vehicle fault. Furthermore, the act of selecting the vehicle feature from a predefined set of vehicle features may include removing from the predefined set of vehicle features a vehicle feature having statistically or substantially equivalent probability distributions for the first vehicle fault and for the second vehicle fault.

The method may additionally include removing from the predefined set of vehicle features a vehicle feature having a sufficient correlation to the removed vehicle feature.

The sufficient correlation may be determined via Pearson correlation coefficient distribution analysis.

The identifying an effective repair of the first vehicle fault may include identifying passage of at least one of a predetermined duration of time and a predetermined distance traveled by the vehicle after repair without recurrence of the first vehicle fault.

The identifying and classifying indistinguishable labor codes may include forming a labor code versus ground truth class confidence matrix and forming a labor code versus ground truth class identity matrix therefrom.

The identifying and classifying indistinguishable labor codes further may include performing hierarchical labor code classification via merging classes in the formed labor code versus ground truth class identity matrix.

The identifying and classifying indistinguishable labor codes may further include refining labor code classification via Bayesian inference analysis.

Also disclosed is a computer-readable medium storing an executable algorithm configured to, upon execution by a processor, perform the above root cause diagnosis of vehicle fault data.

DETAILED DESCRIPTION

Referring to the drawings, wherein like reference numbers refer to like components,FIG. 1shows a schematic view of a motor vehicle10. As shown, the motor vehicle10has a vehicle body12. The vehicle10may be used to traverse a road surface14via a plurality of road wheels16powered by the vehicle's powertrain. Although four wheels16are shown inFIG. 1, a vehicle with fewer or greater number of wheels, or having other means, such as tracks (not shown), of traversing the road surface or other portions of the terrain14is also envisioned. The vehicle10may include a number of mechanical, electrical, and other systems18, such as the vehicle powertrain; heating, ventilation, and air conditioning (HVAC) system; and infotainment system, all arranged on and/or mounted to the vehicle body12.

Such systems18may experience various concerns, issues, or faults during operation of the vehicle10. Some system18faults may cause the vehicle10to become immobile, while other system18faults are less catastrophic, but may still result in user dissatisfaction with the vehicle. Vehicle system18faults may be intermittent. Such intermittent faults may cause temporary loss of system18functionality, they may also set a diagnostic trouble code, but then recover by themselves. A vehicle system18fault is typically addressed by a qualified service technician at a vehicle service center or a repair shop. Depending on whether the vehicle10is covered by a manufacturer's or a third party warranty, the cost of the repair may be covered by either the warranty or the vehicle's owner. However, intermittent system18faults are difficult to diagnose or duplicate, which may require the owner's repeat visits to the service center, and increase warranty costs.

A fleet10A of similar vehicles, i.e., having the system18in common, such as the vehicle10, and repairs of system18faults among the fleet10A may be monitored using a database20supported by a programmable central computer22or an information technology (IT) cloud platform24(shown inFIG. 1). Generally, an IT cloud platform is a provider-managed suite of hardware and software. An IT paradigm enables universal access to shared pools of configurable system resources and higher-level services that may be rapidly provisioned with minimal management effort, often over the Internet. Furthermore, cloud computing relies on sharing of resources to achieve coherence and economies of scale, similar to a public utility. The IT cloud platform24may also be employed in communication with the central computer22for coordinating and managing operation of such a fleet10A of vehicles10.

The central computer22is arranged remotely from the fleet10A. The central computer22includes a memory that is tangible and non-transitory. The memory may be a recordable medium that participates in providing computer-readable data or process instructions. Such a medium may take many forms, including but not limited to non-volatile media and volatile media. Non-volatile media used by the central computer22may include, for example, optical or magnetic disks and other persistent memory. Volatile media may include, for example, dynamic random access memory (DRAM), which may constitute a main memory. Such instructions may be transmitted by one or more transmission medium, including coaxial cables, copper wire and fiber optics, including the wires that comprise a system bus coupled to an electronic processor22A of the central computer22. Memory of the central computer22may also include a flexible disk, hard disk, magnetic tape, other magnetic medium, a CD-ROM, DVD, other optical medium, etc. The central computer22may be equipped with a high-speed primary clock, requisite Analog-to-Digital (A/D) and/or Digital-to-Analog (D/A) circuitry, input/output circuitry and devices (I/O), as well as appropriate signal conditioning and/or buffer circuitry. Algorithms required by the central computer22or accessible thereby may be stored in the memory and automatically executed to provide the required functionality.

The database20may be accessible via a single computer26or via a plurality of similar linked computers, as shown inFIG. 1. Either the central computer22or the IT cloud platform24is configured to employ or access a computer-readable medium30storing an executable algorithm32configured, upon execution by a processor, such as the electronic processor22A, to perform root cause diagnosis34of fault data from the vehicle10and the fleet10A. The root cause diagnosis34may be specifically used to diagnose intermittent system18faults. Accordingly, the algorithm32may be programmed into the electronic processor22A of the central computer22(not shown) or embedded into a discrete accessible computer-readable medium (CRM), as shown inFIG. 1. The algorithm32is used to analyze real time first vehicle fault36data. The algorithm32includes identifying a first vehicle fault36-1(shown inFIG. 2). The algorithm32also includes selecting from field repair data, for example from data of repairs performed on the vehicles10in the fleet10A and stored in the database20, a vehicle feature38-1corresponding to the identified first vehicle fault36-1. The selected vehicle feature38-1corresponds to or is part of a particular system18.

In one embodiment, selecting the vehicle feature38-1corresponding to the identified first vehicle fault36-1may include selecting the vehicle feature from a predefined set38A of vehicle features (shown inFIG. 1). Selecting the vehicle feature38-1from the predefined set38A of vehicle features via the algorithm32may include identifying a second vehicle fault36-2that is unrelated to the first vehicle fault36-1, i.e., has a known different root cause, as shown inFIG. 2depicting first and second vehicle faults plotted versus probability distribution. Also, in such an embodiment, selecting the vehicle feature38-1from the predefined set38A would also include comparing probability distributions of the vehicle features from the predefined set38A of vehicle features for the first vehicle fault36-1and for the second vehicle fault36-2.

Furthermore, in the above embodiment, the algorithm32may include removing from the predefined set38A of vehicle features, i.e., removing from consideration or isolating, a vehicle feature38-2having statistically or substantially equivalent probability distributions for the first vehicle fault36-1and for the second vehicle fault36-2. A comparison of the probability distributions of the vehicle features for the first and second vehicle faults36-1,36-2may be performed via a Jensen-Shannon Divergence (JSD) analysis. The Jensen-Shannon Divergence analysis for the first and second vehicle faults36-1,36-2may be expressed as follows:

As shown inFIG. 3, the algorithm32may additionally include removing from the predefined set38A of vehicle features a vehicle feature38-3having a sufficient correlation to the removed vehicle feature38-2. Specifically, the sufficient correlation may be determined via Pearson correlation coefficient (PCC) distribution analysis. Generally, in statistics, the Pearson correlation coefficient, also referred to as Pearson's r, the Pearson product-moment correlation coefficient (PPMCC) or the bivariate correlation, is a measure of the linear correlation between two variables X and Y. Owing to the Cauchy-Schwarz inequality it has a value between +1 and −1, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation. The particular correlation may, for example, be considered “sufficient” above a specific minimum value, such as greater than 0.7. With reference toFIG. 3, Pearson correlation coefficient analysis for the vehicle features38-2,38-3may be expressed as follows:

PCC⁡(P⁢⁢1,P⁢⁢2)=n⁢∑P⁢⁢1⁢(i)⁢P⁢⁢2⁢(i)-∑P⁢⁢1⁢(i)⁢∑P⁢⁢2⁢(i)n⁢∑P⁢⁢12⁢(i)-(∑P⁢⁢1⁢(i))2⁢n⁢∑P⁢⁢22⁢(i)-(∑P⁢⁢2⁢(i))2where n is the sample size, andwherein P1 and P2 are probability distributions for the respective vehicle features38-2,38-3referenced in the present disclosure.

The algorithm32also includes identifying from the field repair data, for example via the electronic processor22A, such as part of the central computer22, an effective repair40(shown inFIG. 1) of the first vehicle fault36-1. Identifying an effective repair40of the first vehicle fault36-1may include identifying passage of at least one of a predetermined duration of time T and a predetermined distance D traveled by the vehicle10after the repair40without recurrence of the first vehicle fault36-1. The algorithm32additionally includes training and testing, and thereby establishing a labor code (LC) classifier42, as shown inFIG. 4. The algorithm32employs an embedded artificial intelligence (AI) or machine learning algorithm32A, such as a trainable artificial neural network, which may be employed by the central computer22, and specifically embedded in or paired with the executable computer algorithm32. The machine learning algorithm32A is specifically configured to assess and learn from the incoming field repair data to establish the labor code classifier42. Specifically, the training and testing of the labor code classifier42is achieved by using the identified effective repair40of the first vehicle fault36-1and the selected vehicle feature38-1corresponding to the identified first vehicle fault36-1. Labor codes44are generally used by the database20to identify various repairs of vehicle systems, such as the system18.

The algorithm32also includes identifying and classifying (and thereby isolating), using the trained labor code classifier42, indistinguishable labor codes44A, i.e., which are ambiguous or indistinct from other labor codes44based on the results of testing performed via the machine learning algorithm32A. As shown inFIG. 4, identifying and classifying indistinguishable labor codes44A may include forming a labor code versus ground truth class confidence matrix46and forming a labor code versus ground truth class identity matrix48therefrom. As shown inFIG. 5, identifying and classifying indistinguishable labor codes44A may further include performing hierarchical labor code classification via merging classes in the formed labor code versus ground truth class identity matrix48to form a labor code recommendation matrix50. Specifically, inFIGS. 4 and 5, labor codes LC5and LC6represent indistinguishable labor codes44A.

As shown inFIG. 6, identifying and classifying indistinguishable labor codes44A may further include refining labor code classification via forming a class confidence matrix52and applying Bayesian inference analysis. Specifically, Bayesian inference analysis may be applied for further refinement when the labor code classifier reports LC5, and the outcome as accurate, but when LC6is reported, there is a possibility that the pattern belongs to either LC5or LC6. Similar toFIGS. 4 and 5, inFIG. 6the labor codes LC5and LC6represent indistinguishable labor codes44A. In general, Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian inference derives the posterior probability as a consequence of two antecedents: a prior probability and a “likelihood function” derived from a statistical model for the observed data. Bayesian inference computes the posterior probability according to Bayes' theorem. With continued reference toFIG. 6, Bayesian inference analysis for the indistinguishable labor codes44A may be expressed as follows:P(Report=LC6|Truth=LC5)=0.46P(Truth=LCi), i=0, 5, 6 is the known probability distribution assuming P(Truth)=Li)=⅓, i=0, 5, 6)

Following identifying and classifying the indistinguishable labor codes44A the algorithm32further includes storing in the database20or on a server54(shown inFIG. 1) connected to the central computer22the identified and classified indistinguishable labor codes44A. The database20and/or the server54may be accessed by remote computer(s)26. The computer26may be part of a computer network56(shown inFIG. 1) in electronic communication with the database20, located in a service center, and accessible by a technician. As a result, the identified and classified indistinguishable labor codes44A may be communicated on demand to a service technician for performing a diagnosis and subsequent repair of a root cause of real time first vehicle fault36-1data.

FIG. 7depicts a method100of root cause diagnosis34of fault data from the vehicle10and the fleet10A, as described above with respect toFIGS. 1-6. As described above, the method100is intended to be embodied in the algorithm32and employ machine learning to perform the root cause diagnosis34. As discussed above, the root cause diagnosis34may be specifically used to diagnose and repair intermittent system18faults.

The method100initiates in frame102with identifying the first vehicle fault36-1. Following frame102, the method proceeds to frame104. In frame104, the method includes selecting from the field, such as the vehicle fleet10A, repair data, via the electronic processor22A, for example, of the central controller22, the vehicle feature38-1corresponding to the identified first vehicle fault36-1. As described above, selecting the vehicle feature38-1from field repair data may include selecting the field repair data from or corresponding to the vehicle fleet10A.

Additionally, selecting the vehicle feature38-1corresponding to the identified first vehicle fault36-1may include selecting the vehicle feature from a predefined set38A of vehicle features. Selecting the vehicle feature38-1from the predefined set38A of vehicle features via the algorithm32may include identifying the second vehicle fault36-2that is unrelated to the first vehicle fault36-1. Also, selecting the vehicle feature38-1from the predefined set38A would also include comparing probability distributions of the vehicle features from the predefined set38A of vehicle features for the first vehicle fault36-1and for the second vehicle fault36-2.

Furthermore, the algorithm32may include removing from the predefined set38A of vehicle features the vehicle feature38-2having substantially or statistically equivalent probability distributions for the first vehicle fault36-1and for the second vehicle fault36-2. As described above, such a comparison of the probability distributions of the vehicle features for the first and second vehicle faults36-1,36-1may be performed via a Jensen-Shannon Divergence analysis. Additionally, removing from the predefined set38A of vehicle features a vehicle feature38-3having a sufficient, correlation to the removed vehicle feature38-2may be determined via the Pearson correlation coefficient distribution analysis.

In frame104, the method may further include removing from the predefined set38A of vehicle features the vehicle feature having a sufficient or sufficiently high correlation to the removed vehicle feature38-2. As described above, the sufficient correlation may be determined via Pearson correlation coefficient distribution analysis. After frame104, the method advances to frame106. In frame106, the method includes identifying from the field repair data, such as via the electronic processor22A, effective repair40of the identified first vehicle fault36-1. Identifying effective repair40of the first vehicle fault36-1may include identifying passage of at least one of a predetermined duration of time T and a predetermined distance D traveled by the vehicle10after the repair40without recurrence of the first vehicle fault36-1.

Following frame106, the method proceeds to frame108. In frame108the method includes establishing, such as by training and testing via the machine learning algorithm32A, the labor code classifier42using the identified effective repair40of the first vehicle fault36-1and the selected vehicle feature38-1corresponding to the identified first vehicle fault. After frame108, the method advances to frame110. As described above with respect toFIGS. 1-6, in frame110the method includes identifying and classifying, such as via the electronic processor22A, using the trained labor code classifier42to thereby isolate indistinguishable labor codes44A.

Identifying and classifying indistinguishable labor codes44A in frame110may include forming a labor code versus ground truth class confidence matrix46and forming a labor code versus ground truth class identity matrix48therefrom. Identifying and classifying indistinguishable labor codes44A may also include performing hierarchical labor code classification via merging classes in the formed labor code versus ground truth class identity matrix48. Furthermore, identifying and classifying indistinguishable labor codes44A may include refining labor code classification via Bayesian inference analysis. After frame110, the method advances to frame112. In frame112the method includes communicating the identified and classified indistinguishable labor codes44A for diagnosing a root cause of real time first vehicle fault36-1data. Following frame112, the method may return to frame102for identifying an additional fault in the vehicle10, i.e., different from the previously identified first vehicle fault36-1, for similar analysis.