Systems and methods for predictive reliability mining

• US 10,248,490 B2
• Filed: 03/01/2016
• Issued: 04/02/2019
• Est. Priority Date: 10/15/2015
• Status: Active Grant

• Current Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Original Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Inventors: Singh, Karamjit, Shroff, Gautam, Agarwal, Puneet
• Primary Examiner(s): Leibovich, Yair
• Application Number:
  US15/057,882
• Publication Number:
  US 20170109222A1
• Time in Patent Office: 1,127 Days
• Field of Search: 14 37
• CPC Class Codes: G05B 23/0275 Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
• CPC Class Codes: G06F 11/0709 in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
• CPC Class Codes: G06F 11/0751 Error or fault detection not based on redundancy power supply failures G06F1/30; network fault management H04L41/06
• CPC Class Codes: G06F 11/0772 Means for error signaling, e.g. using interrupts, exception flags, dedicated error registers
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C

Abstract

Systems and methods for predictive reliability mining are provided that enable predicting of unexpected emerging failures in future without waiting for actual failures to start occurring in significant numbers. Sets of discriminative Diagnostic Trouble Codes (DTCs) from connected machines in a population are identified before failure of the associated parts. A temporal conditional dependence model based on the temporal dependence between the failure of the parts from past failure data and the identified sets of discriminative DTCs is generated. Future failures are predicted based on the generated temporal conditional dependence and root cause analysis of the predicted future failures is performed for predictive reliability mining. The probability of failure is computed based on both occurrence and non-occurrence of DTCs. The root cause analysis enables identifying a subset of the population when an early warning is generated and also when an early warning is not generated.

Claims

1. A computer implemented method for predictive reliability mining in a population of connected machines, the method comprising:

identifying sets of discriminative Diagnostic Trouble Codes (DTCs) from DTCs generated preceding failure, the sets of discriminative DTCs corresponding to associated pre-defined parts of the connected machines;

generating a temporal conditional dependence model based on temporal dependence between failure of the pre-defined parts from past failure data and the identified sets of discriminative DTCs;

segregating the population of connected machines into a first set comprising connected machines in which DTCs are not generated in a given time period and a second set comprising connected machines in which at least one DTC is generated in the given time period; and

predicting future failures based on the generated temporal conditional dependence model and occurrence and non-occurrence of DTCs in the segregated population of connected machines.

2. The computer implemented method of claim 1 further comprising a step of performing root cause analysis of the predicted future failures for predictive reliability mining.

3. The computer implemented method of claim 2, wherein the step of performing root cause analysis comprises identifying a subset of the population when an early warning for at least one of the pre-defined parts is generated.

4. The computer implemented method of claim 2, wherein the step of performing root cause analysis comprises identifying a subset of the population with a possible anomaly when an early warning is not generated at the population level.

5. The computer implemented method of claim 4, wherein the step of performing root cause analysis comprises identifying a subset of the population with a possible anomaly when an early warning is not generated at the population level comprises:

(i) defining an anomaly score for each of the connected machines;

(ii) associating each of the connected machines with a record comprising a set of pre-defined features and the defined anomaly score;

(iii) discretizing the defined anomaly score into either a pre-defined high level or a normal level;

(iv) performing association rule mining to identify association rules with the high level anomaly score;

(v) clustering the identified association rules using a density based technique to form rule clusters;

(vi) selecting one or more rules from each of the rule clusters that have high support and confidence; and

(vii) identifying the subset of the population with the high level anomaly score based on antecedents of the selected one or more rules that are indicative of potential reasons for the high level anomaly score.

6. The computer implemented method of claim 1, wherein the step of identifying sets of discriminative DTCs is based on association rule mining.

7. The computer implemented method of claim 6, wherein the association rule mining comprises use of Apriori technique.

8. The computer implemented method of claim 6, wherein antecedents of rules identified by the association rule mining technique form the set of discriminative DTCs.

9. The computer implemented method of claim 1, wherein the temporal conditional dependence model is a Bayesian network.

10. The computer implemented method of claim 1, wherein the temporal dependence between failure of the pre-defined parts from past failure data and the identified sets of discriminative DTCs follows a Weibull distribution.

11. The computer implemented method of claim 1, wherein the step of predicting future failures comprises the step of computing the probability of failure based on both occurrence and non-occurrence of DTCs in the segregated population of connected machines and generating an early warning when predicted number of failures are greater than expected number of failures based on the past failure data by a pre-defined value.

12. The computer implemented method of claim 11, wherein the pre-defined value is based on the predicted number of failures and variance of a random variable representing number of failures of the pre-defined parts in a given time period.

13. The computer implemented method of claim 1, wherein the step of performing root cause analysis when an early warning for at least one of the pre-defined parts is generated comprises:

(i) calculating a first expected time of failure based on the past failure data;

(ii) calculating a second expected time of failure based on expected time of failure of the second set of the segregated population that is further based on pre-defined delay parameters and occurrence time of the at least one DTC;

(iii) defining an anomaly score for each of the connected machines based on the calculated first expected time of failure and the second expected time of failure;

(iv) iteratively performing steps (i) through (iv) for pre-defined features of the connected machines; and

(v) identifying the subset of the population having the anomaly score greater than a pre-defined threshold, the identified subset indicating possible reasons for the early warning for each of the pre-defined features.

14. A system for predictive reliability mining in a population of connected machines, the system comprising:

one or more hardware processors;

a communication interface device;

one or more internal data storage devices operatively coupled to the one or more processors for storing;

an input module configured to receive Diagnostic Trouble Codes (DTCs) from on-board diagnostic systems of pre-defined parts of the connected machines;

a DTC pattern identifier configured to identify sets of discriminative DTCs from the DTCs generated preceding failure, the sets of discriminative DTCs corresponding to associated pre-defined parts of the connected machines;

a Bayesian network generator configured to;

generate a temporal conditional dependence model based on temporal dependence between failure of the pre-defined parts from past failure data and the identified sets of discriminative DTCs; and

segregate the population of connected machines into a first set comprising connected machines in which DTCs are not generated in a given time period and a second set comprising connected machines in which at least one DTC is generated in the given time period; and

a failure predictor configured to predict future failures based on the generated temporal conditional dependence and occurrence and non-occurrence of DTCs in the segregated population of connected machines.

15. The system for predictive reliability mining of claim 14 further comprising an analyzer configured to perform root cause analysis of the predicted future failures for predictive reliability mining.

16. A computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:

identify sets of discriminative Diagnostic Trouble Codes (DTCs) from DTCs generated preceding failure, the sets of discriminative DTCs corresponding to associated pre-defined parts of the connected machines;

generate a temporal conditional dependence model based on temporal dependence between failure of the pre-defined parts from past failure data and the identified sets of discriminative DTCs;

segregate the population of connected machines into a first set comprising connected machines in which DTCs are not generated in a given time period and a second set comprising connected machines in which at least one DTC is generated in the given time period; and

predict future failures based on the generated temporal conditional dependence model and occurrence and non-occurrence of DTCs in the segregated population of connected machines.