SYSTEM AND METHOD FOR AUTOMATIC FAULT DETECTION OF A MACHINE

• US 20110035094A1
• Filed: 08/04/2009
• Published: 02/10/2011
• Est. Priority Date: 08/04/2009
• Status: Abandoned Application

• Current Assignee: Telcordia Technologies Incorporated (Telefonaktiebolaget LM Ericsson)
• Original Assignee: Telcordia Technologies Incorporated (Telefonaktiebolaget LM Ericsson)
• Inventors: Van Den Berg, Eric, McIntosh, Allen
• Application Number:
  US12/535,255
• Publication Number:
  US 20110035094A1
• Time in Patent Office: Days
• US Class Current: 701/33
• CPC Class Codes: G05B 23/0251 Abstraction hierarchy, e.g. "complex systems", i.e. system is divided in subsystems, subsystems are monitored and results are combined to decide on status of whole system
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005

Abstract

A system and method for automatic fault detection of a machine is described. In one embodiment, a semantic structure is constructed using the words and values associated with the parameter identification numbers used by the on-board diagnostic system in a vehicle. The semantic structure is enhanced, analyzed, and reduced to determine the number and arrangement of the clusters that should be independently analyzed in order to produce the most reliable results in a computationally efficient manner. Each cluster is then used to detect outliers that are used to detect vehicle malfunctions.

Claims

1. A method for automatically detecting faults in a machine, comprising the steps of:

collecting a plurality of diagnostic data from the machine, the diagnostic data including a plurality of descriptions and a plurality of numerical values, each numerical value associated with a select one of the descriptions and representing a measurement from the machine;

analyzing the descriptions of the diagnostic data to formulate a semantic structure representing the machine;

grouping the semantic space into k clusters, each cluster having semantic-similar descriptions; and

for each cluster, detecting anomalies by using the measurement data associated with all the descriptions in the cluster, the anomalies for use in indicating a machine fault.

2. The method of claim 1 further comprising the step of:

collecting the diagnostic data from the machine during operation of the machine.

3. The method of claim 1, the analyzing step further comprising the step of:

formulating the semantic structure from a frequency analysis of semantically-significant words used in the descriptions.

4. The method of claim 3, the analyzing step further comprising the step of:

adjusting the semantic structure to include correlations of the numerical values associated with the descriptions.

5. The method of claim 1, wherein the descriptions are word phrases used to describe parameter identification numbers associated with an on-board diagnostic system.

6. A computer readable storage medium for use in automatically detecting machine faults, said apparatus comprising:

a similarity graph having a plurality of edges and vertices, each vertex v_i representing a description of a diagnostic parameter associated with the machine, and each edge weighted by s_ij, where s_ij represents a similarity score between vertex v_i and vertex v_j;

a first procedure that finds k groups of connected components in the graph, where each edge in a group has a high similarity score;

a second procedure that generates a subset of the graph, UK, representing the groups of connected components;

a clustering procedure that groups UK into k clusters, where each cluster k represents similarly-situated descriptions that are used to identify anomalies indicative of a machine fault within the cluster.

7. The apparatus of claim 6, further comprising:

a term-document matrix of size N×

M and having entries, d_ab, where M denotes a number of terms taken from the N descriptions, each entry d_ab, representing a frequency score between description a and term b.

8. The apparatus of claim 7, further comprising:

a similarity matrix of size N×

N, having entries w_ij, where N denotes a number of the descriptions, and w_ij denotes a score indicating a semantic similarity between description i and description j;

a third procedure that constructs the similarity matrix from the term-document matrix; and

a fourth procedure that constructs the similarity graph from the similarity matrix.

9. The apparatus of claim 8, wherein the third procedure constructs the similarity matrix as a combination of a plurality of additional similarity matrices, each additional similarity matrix representing data associated with the machine.

10. The apparatus of claim 6, wherein the similarity graph is a normalized graph Laplacian, L.

11. The apparatus of claim 10, wherein the first procedure further comprises:

a singular value decomposition procedure that is applied to the similarity graph, L, thereby generating a diagonal matrix D, and matrix U.

12. The apparatus of claim 11, wherein the first procedure

determines the value k equal to a number of zero-valued singular values in D.

13. The apparatus of claim 11, wherein the second procedure

forms UK, having N rows of U and last k columns of U.

14. The apparatus of claim 6, further comprising:

a diagnostic sensor collection procedure that collects diagnostic data from the machine, the diagnostic data containing the descriptions.

15. The apparatus of claim 6, further comprising:

a fault detection procedure that analyzes measurement data from the machine that corresponds to each description in a cluster for anomalies indicative of the machine faults.

16. A system for automatically detecting machine faults, said apparatus comprising:

a first processor that senses and collects diagnostic data from the machine, the diagnostic data having a plurality of descriptions and measurement values;

a second processor, in communication with the first processor, that

receives the diagnostic data from the first processor,

generates a semantic structure of the machine from the descriptions,

groups the semantic structure into k clusters, each cluster having semantically-situated descriptions,

for each cluster, analyzes the measurement values associated with the descriptions included in each cluster for outliers that can be indicative of a machine fault.

17. The system of claim 16,

wherein the second processor uses a normalized graph Laplacian to generate a semantic structure of the machine.

18. The system of claim 17,

wherein the second processor applies a singular value decomposition to the normalized graph Laplacian to determine k.

19. The system of claim 16,

wherein the second processor applies a singular value decomposition to the normalized graph Laplacian to generate a subset of the semantic structure, UK, that is used to generate the k clusters.

20. The system of claim 19,

wherein the second processor uses a k-means clustering to generate k clusters from UK.