A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction

Abstract

Industries increasingly rely on intricate multi-component systems, necessitating efficient maintenance strategies to ensure system reliability and minimize downtime. Predictive maintenance, an emerging approach that utilizes data-driven techniques to forecast and prevent failures, holds significant potential in this regard. This paper presents a predictive maintenance strategy tailored specifically for multi-component systems. In order to accurately anticipate the remaining useful life (RUL) of components, we develop a method that combines data and model fusion based on a particle filtering approach and a degradation distribution model. By integrating degradation data with models, our method outperforms traditional model-based approaches in terms of prediction accuracy. Subsequently, we apply an optimized maintenance model to individual components based on the trigger threshold for RUL. This model determines the most optimal maintenance actions for each component, with the aim of minimizing maintenance costs. Furthermore, we introduce an optimized maintenance strategy that incorporates opportunistic maintenance to further reduce the overall maintenance cost of the system. This strategy leverages predicted RUL information to schedule proactive maintenance actions at the opportune moment, resulting in a significant cost reduction compared to traditional periodic maintenance approaches. To validate the feasibility and effectiveness of our proposed strategy, we utilize experimental data from open-source lithium-ion batteries at the NASA PCoE Center. Through this empirical validation, we provide real-world evidence showcasing the applicability and performance of our strategy in a multi-component system.

A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction

“…However, not all sensors contribute useful information for RUL prediction, as some remain constant until failure [36,40,47]. Following the approach outlined in [36], we selectively incorporate data from 14 sensors (sensors 2, 3,4,7,8,9,11,12,13,14,15,17,20,21) into our training and testing processes. Additionally, we apply max-min normalization to the sensor readings, which is expressed by the formula [36]:…”

“…Physics-based methods employ mathematical tools such as differential equations to model the degradation process of a system, offering insights into the physical mechanisms governing its deterioration [3][4][5][6][7][8][9][10]. On the other hand, statistics-based methods rely on probabilistic models, such as the Bayesian hidden Markov model (HMM), to approximate the underlying degradation process [11][12][13][14][15][16]. Nevertheless, these conventional methods either depend on prior knowledge of system degradation mechanics or rest on probabilistic assumptions about the underlying statistical degradation processes.…”

Bidirectional LSTM Autoencoder Transformer for Remaining Useful Life Estimation

“…The outputs from these multiple heads are then concatenated and linearly transformed to produce the final output. Formally, the multi-head attention is defined as: 𝑀𝑢𝑙𝑡𝑖𝐻𝑒𝑎𝑑(𝑄, 𝐾, 𝑉) = 𝐶𝑜𝑛𝑐𝑎𝑡(ℎ𝑒𝑎𝑑 1 , ℎ𝑒𝑎𝑑 2 , … , ℎ𝑒𝑎𝑑 ℎ )𝑊 𝑜 (6) where ℎ𝑒𝑎𝑑 𝑖 = 𝐴𝑡𝑡𝑒𝑛𝑡𝑖𝑜𝑛(𝑄𝑊 𝑄 𝑖 , 𝐾𝑊 𝑘 𝑖 , 𝑉𝑊 𝑉 𝑖 ) , and 𝑊 𝑄 𝑖 , 𝑊 𝑘 𝑖 , 𝑊 𝑉 𝑖 and 𝑊 𝑜 are all learnable weight matrices.…”

Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines

“…Existing RUL prediction models generally fall within two primary categories: the model-based [1,2] and the data-driven approaches [3,4]. The model-based approach relies on a certain level of physical knowledge about machine degradation to predict RUL, such as employing theories of the Paris law for bearing defect growth [5] and reliability laws [6][7][8].…”