Accurate fault information is critical for optimal scheduling of production activities, improving system reliability, and reducing operation and maintenance cost. In recent years, many fault diagnosis methods for rolling element bearings have been developed based on deep learning. Most of them are totally data-driven and do not consider the domain knowledge that has been used in fault diagnosis for years. Meanwhile, operating conditions such as rotating speed and load that have great influence on vibration signals are also ignored. It may cause a decrease in accuracy when bearing type or operating condition changes. To address these problems, this paper proposes a rolling element bearing fault diagnosis and localization approach based on multi-task convolutional neural network (CNN) with information fusion. In the proposed approach, domain knowledge, operating conditions, and vibration signals are fused into a 3-dimensional input that can be processed well by CNN. Then a multi-task CNN with dynamic training rates is constructed to simultaneously accomplish two tasks, fault diagnosis and localization. Experimental results on two rolling element bearing testbeds with different bearing types and operating conditions are presented and compared with existing state-of-the-art approaches to demonstrate the effectiveness and accuracy of the proposed approach.

Prognostics and health management (PHM) has garnered significant attention in industrial fields, particularly due to its successful application in managing battery degradation. However, current approaches are inadequate in addressing multiple thresholds, including both theoretical formulation and practical computational complexity. These limitations hinder the development and implementation of threshold-varying assessments, thereby impeding the advancement of PHM application. This article investigates prognostic applications with different failure thresholds and highlights the importance of failure threshold selection. In addition, theoretical evaluation and analysis are provided for multiple threshold settings, encompassing both discrete and continuous series. This introduces a novel technical domain for prognostic applications. The effectiveness of threshold-varying assessment is verified with several different approaches on real battery degradation experiments. Furthermore, we demonstrate the practical significance of threshold-varying assessments in enabling on-demand scheduling for maintenance or replacement of spare parts. Most importantly, to meet the real-time requirements of practical prognostic applications, this article also discusses the computational complexity of threshold-varying assessment and finds an applicable solution for this common difficulty.

Lebesgue-sampling based fault diagnosis and prognosis (LS-FDP) is developed with advantages of light computation and small uncertainty accumulation. In previous research, the applications of LS-FDP are developed for single-model degradation processes, which do not consider degradation processes that involve multiple modes with switching models. To address this scenario, this paper proposes a modified interacting multiple model (IMM) filter in the Lebesgue-Time-Space-Model (LTSM) based FDP framework. The design, analysis, and particle filtering based implementation of the proposed method are discussed in detail. A simulation example and a real experiment are presented to demonstrate the accuracy, effectiveness, robustness, and universality of the proposed method.

The performance of fault diagnosis and prognosis (FDP) relies greatly on the model that describes the fault behavior and dynamics under study. In the past decades, many researches focused on establishing reliable, robust, and accurate models to describe fault growth dynamics. However, uncertainties in the modeling process severely affect the accuracy and precision of FDP results. A major challenge in FDP is how to deal with the uncertainties that affect the performance of the system. For commonly used state-space model, uncertainties exist in both state transition equation and the measurement equation. In this article, the influence of uncertainties on FDP results is studied and compared in terms of accuracy and precision. Furthermore, this article proposes a differential model decomposition approach, which can automatically acquire appropriate model parameters and uncertainties for the state-space model. The effectiveness and advantages of the proposed method are verified and demonstrated with three different application scenarios, including comparison studies on public data and online experimental verification.