Multiform Informed Machine Learning Based on Piecewise and Weibull for Engine Remaining Useful Life Prediction

Abstract

Informed machine learning (IML), which strengthens machine learning (ML) models by incorporating external knowledge, can get around issues like prediction outputs that do not follow natural laws and models, hitting optimization limits. It is therefore of significant importance to investigate how domain knowledge of equipment degradation or failure can be incorporated into machine learning models to achieve more accurate and more interpretable predictions of the remaining useful life (RUL) of equipment. Based on the informed machine learning process, the model proposed in this paper is divided into the following three steps: (1) determine the sources of the two types of knowledge based on the device domain knowledge, (2) express the two forms of knowledge formally in Piecewise and Weibull, respectively, and (3) select different ways of integrating them into the machine learning pipeline based on the results of the formal expression of the two types of knowledge in the previous step. The experimental results show that the model has a simpler and more general structure than existing machine learning models and that it has higher accuracy and more stable performance in most datasets, particularly those with complex operational conditions, which demonstrates the effectiveness of the method in this paper on the C-MAPSS dataset and assists scholars in properly using domain knowledge to deal with the problem of insufficient training data.

Uninhibited Positional and Contextual Attention in Spectral-Based (SPT) Transformer with Multi-head Shortcut for Improved Remaining Useful Life Forecasting in Industry 4.0

“…The SPT model balances computing efficiency and performance using DCTA and multi-head shortcut (MHS) techniques. The IML [23] Model facilitates the integration of domain knowledge into RUL forecasts. The SPT model utilizes attention mechanisms to effectively forecast outcomes without requiring domain knowledge integration.…”

“…The approach consists of three steps. The study involves the identification of knowledge sources, formalization using Piecewise and Weibull expressions, and integration into the machine learning pipeline [23].…”