Abstract
This study presents the methods employed by a team from the department of Mechatronics and Dynamics at the University of Paderborn, Germany for the 2013 PHM data challenge.The focus of the challenge was on maintenance action recommendation for an industrial equipment based on remote monitoring and diagnosis. Since an ensemble of data driven methods has been considered as the state of the art approach in diagnosis and prognosis, the first approach was to evaluate the performance of an ensemble of data driven methods using the parametric data as input and problems (recommended maintenance action) as the output. Due to close correlation of parametric data of different problems, this approach produced high misclassification rate. Event-based decision trees were then constructed to identify problems associated with particular events. To distinguish between problems associated with events that appeared in multiple problems, support vector machine (SVM) with parameters optimally tuned using particle swarm optimization (PSO) was employed. Parametric datawas used as the input to the SVM algorithm and majority voting was employed to determine the final decision for cases with multiple events. A total of 165 SVM models were constructed.This approach improved the overall score from 21 to 48. The method was further enhanced by employing an ensemble of three data driven methods, that is, SVM, random forests (RF) and bagged trees (BT), to build the event based models. With this approach, a score of 51 was obtained . The results demonstrate that the proposed event based method can be effective in maintenance action recommendation based on events codes and parametric data acquired remotely from an industrial equipment.
Highlights
The focus of the 2013 Prognostic and Health Management data challenge was on maintenance action recommendation in industrial remote monitoring and diagnostics
The challenge was to identify faults with confirmed maintenance actions masked within a huge amount of data with unconfirmed problems, given event codes and associated snapshot of the operational data acquired when a trigger condition is met on board
Random forests (RF): Random forests is derived from Classification and Regression Trees (CART) and it involves iteratively training a number of classification trees with each tree trained with a data set that is randomly selected with replacement from the original data set
Summary
The focus of the 2013 Prognostic and Health Management data challenge was on maintenance action recommendation in industrial remote monitoring and diagnostics. Remote monitoring and diagnosis is currently gaining momentum in condition based maintenance due to the advancement in information technology and telecommunication industry(Xue & Yan, 2007). It is evident that the use of operational data obtained remotely poses a huge challenge in fault identification and classification and there is need to develop algorithms that are capable of exploiting the operational data to detect abnormalities in industrial equipment, and to classify faults and recommend maintenance action. The following sections describe the data used in the challenge and data preprocessing
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