Machine learning-based failure prediction in industrial maintenance: improving performance by sliding window selection

Abstract

PurposeMachine learning (ML) models are increasingly being used in industrial maintenance to predict system failures. However, less is known about how the time windows for reading data and making predictions affect performance. Therefore, the purpose of this research is to assess the impact of different sliding windows on prediction performance.Design/methodology/approachThe authors conducted a factorial experiment using high dimensional machine data covering two years of operation, taken from a real industrial case for the production of high-precision milled and turned parts. The impacts of different reading and prediction windows were tested for three ML algorithms (random forest, support vector machines and logistic regression) and four metrics (accuracy, precision, recall and F-score).FindingsThe results reveal (1) the critical role of the prediction window contingent upon the application domain, (2) a non-monotonic relationship between the reading window and performance, and (3) how sliding window selection can systematically be used to improve different facets of performance.Originality/valueThe study's findings advance the knowledge of ML-based failure prediction, by highlighting how systematic variation of two important but yet understudied factors contributes to the development of more useful prediction models.

Predicting Machine Failures From Multivariate Time Series: An Industrial Case Study

“…Predicting failures via manual inspection is time-consuming and costly, especially because the correlation among multiple variables can be difficult to appraise with manual approaches. Computer-aided methods can improve performance and reliability and are less onerous and error-prone [10][11][12].…”

“…3 of 29 [12,17]. DL methods include neural network architectures such as recurrent neural networks (RNN) [19] and convolutional neural networks (CNN) [19].…”

“…RF is one of the most used approaches [12,[14][15][16][17]22,23,25,30,[40][41][42][43][44][45]. The work in [25] compares RF with ML and DL approaches, including Neural Networks and SVM, and shows the superiority of RF in terms of accuracy for a prediction window not longer than 300 seconds for a case study of wind turbines.…”

“…SVM is another approach commonly used in supervised failure prediction [12,14,15,25,26,43,[46][47][48]. The work in [26] presents an application of SVM to railway systems and evaluates the performances using False Positive Rate (FPR) and recall.…”

“…Some studies evaluate the impact of either the reading window length [16,17,26] or the prediction window length [20,23,24]. Only a few [12,26] evaluate the impact of varying the reading and the prediction window. Considering the surveyed approaches, the work in [12] is the only one that proposes a comparison between different ML algorithms varying both the RW and PW and assessing the contribution of each.…”