Weibull recurrent neural networks for failure prognosis using histogram data

• Published in: Neural computing & applications 2023-02, Vol.35 (4), p.3011-3024
• Main Authors: Dhada, Maharshi, Parlikad, Ajith Kumar, Steinert, Olof, Lindgren, Tony
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Clustering; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Heavy duty trucks; Histograms; Image Processing and Computer Vision; Industrial applications; Machine learning; Medical prognosis; Neural networks; Prediction models; Predictive maintenance; Probability and Statistics in Computer Science; Prognosis; Recurrent neural networks; S.I.: Applications of Machine Learning in Maintenance Engineering and Management (IFAC AMEST 2020); Special Issue on Applications of Machine Learning in Maintenance Engineering and Management (IFAC AMEST 2020); Statistical modelling; Weibull distribution
• ISSN: 0941-0643; 1433-3058; 1433-3058
• DOI: 10.1007/s00521-022-07667-7

Weibull time-to-event recurrent neural networks (WTTE-RNN) is a simple and versatile prognosis algorithm that works by optimising a Weibull survival function using a recurrent neural network. It offers the combined benefits of the sequential nature of the recurrent neural network, and the ability of the Weibull loss function to incorporate censored data. The goal of this paper is to present the first industrial use case of WTTE-RNN for prognosis. Prognosis of turbocharger conditions in a fleet of heavy-duty trucks is presented here, where the condition data used in the case study were recorded as a time series of sparsely sampled histograms. The experiments include comparison of the prediction models trained using data from the entire fleet of trucks vs data from clustered sub-fleets, where it is concluded that clustering is only beneficial as long as the training dataset is large enough for the model to not overfit. Moreover, the censored data from assets that did not fail are also shown to be incorporated while optimising the Weibull loss function and improve prediction performance. Overall, this paper concludes that WTTE-RNN-based failure predictions enable predictive maintenance policies, which are enhanced by identifying the sub-fleets of similar trucks.