Multimodal Degradation Prognostics Based on Switching Kalman Filter Ensemble

A performance degradation assessment method for complex electromechanical systems based on adaptive evidential reasoning rule

The evidence reasoning (ER) rule has been widely used in various fields to deal with both quantitative and qualitative information with uncertainty. However, when analyzing dynamic systems, the importance of various indicators frequently changes with time and working conditions, such as performance degradation assessment of complex electromechanical systems, and the weights of the traditional evidence reasoning rules cannot be appropriately adjusted. To solve this problem, this paper proposes an adaptive evidence reasoning (AER) rule that can adjust weights according to different times and working conditions. The AER rule has two unique features: adaptive weight operation under time division and adaptive weight operation under working-condition division, which are used to solve the problem of dynamic weight adjustment under different times and working conditions. The CMA-ES algorithm is used to optimize the model parameters. Two case studies of performance degradation assessment are established to prove the advantage of the AER rule: a computer numerical control experiment and a simulation experiment of turbofan aeroengine. The results verify the effectiveness and practicability of the proposed method.

Remaining Useful Life Prediction Based on Deep Learning: A Survey

Remaining useful life (RUL) is a metric of health state for essential equipment. It plays a significant role in health management. However, RUL is often random and unknown. One type of physics-based method builds a mathematical model for RUL using prior principles, but this is a tough task in real-world applications. Another type of method estimates RUL from available information through condition and health monitoring; this is known as the data-driven method. Traditional data-driven methods require significant human effort in designing health features to represent performance degradation, yet the prediction accuracy is limited. With breakthroughs in various application scenarios in recent years, deep learning techniques provide new insights into this problem. Over the past few years, deep-learning-based RUL prediction has attracted increasing attention from the academic community. Therefore, it is necessary to conduct a survey on deep-learning-based RUL prediction. To ensure a comprehensive survey, the literature is reviewed from three dimensions. Firstly, a unified framework is proposed for deep-learning-based RUL prediction and the models and approaches in the literature are reviewed under this framework. Secondly, detailed estimation processes are compared from the perspective of different deep learning models. Thirdly, the literature is examined from the perspective of specific problems, such as scenarios where the collected data consist of limited labeled data. Finally, the main challenges and future directions are summarized.

Deep Bidirectional Recurrent Neural Networks Ensemble for Remaining Useful Life Prediction of Aircraft Engine

Remaining useful life (RUL) prediction of aircraft engine (AE) is of great importance to improve its reliability and availability, and reduce its maintenance costs. This article proposes a novel deep bidirectional recurrent neural networks (DBRNNs) ensemble method for the RUL prediction of the AEs. In this method, several kinds of DBRNNs with different neuron structures are built to extract hidden features from sensory data. A new customized loss function is designed to evaluate the performance of the DBRNNs, and a series of the RUL values is obtained. Then, these RUL values are reencapsulated into a predicted RUL domain. By updating the weights of elements in the domain, multiple regression decision tree (RDT) models are trained iteratively. These models integrate the predicted results of different DBRNNs to realize the final RUL prognostics with high accuracy. The proposed method is validated by using C-MAPSS datasets from NASA. The experimental results show that the proposed method has achieved more superior performance compared with other existing methods.

Partially Hidden Markov Chain Multivariate Linear Autoregressive model: inference and forecasting—application to machine health prognostics

Time series subject to regime shifts have attracted much interest in domains such as econometry, finance or meteorology. For discrete-valued regimes, models such as the popular Hidden Markov Chain (HMC) describe time series whose state process is unknown at all time-steps. Sometimes, time series are annotated. Thus, another category of models handles the case with regimes observed at all time-steps. We present a novel model which addresses the intermediate case: (i) state processes associated to such time series are modelled by Partially Hidden Markov Chains (PHMCs); (ii) a multivariate linear autoregressive (MLAR) model drives the dynamics of the time series, within each regime. We describe a variant of the expectation maximization (EM) algorithm devoted to PHMC-MLAR model learning. We propose a hidden state inference procedure and a forecasting function adapted to the semi-supervised framework. We first assess inference and prediction performances, and analyze EM convergence times for PHMC-MLAR, using simulated data. We show the benefits of using partially observed states as well as a fully labelled scheme with unreliable labels, to decrease EM convergence times. We highlight the robustness of PHMC-MLAR to labelling errors in inference and prediction tasks. Finally, using turbofan engine data from a NASA repository, we show that PHMC-MLAR outperforms or largely outperforms other models: PHMC and MSAR (Markov Switching AutoRegressive model) for the feature prediction task, PHMC and five out of six recent state-of-the-art methods for the prediction of machine useful remaining life.

Degradation Alignment in Remaining Useful Life Prediction Using Deep Cycle-Consistent Learning

Due to the benefits of reduced maintenance cost and increased operational safety, effective prognostic methods have always been highly demanded in real industries. In the recent years, intelligent data-driven remaining useful life (RUL) prediction approaches have been successfully developed and achieved promising performance. However, the existing methods mostly set hard RUL labels on the training data and pay less attention to the degradation pattern variations of different entities. This article proposes a deep learning-based RUL prediction method. The cycle-consistent learning scheme is proposed to achieve a new representation space, where the data of different entities in similar degradation levels can be well aligned. A first predicting time determination approach is further proposed, which facilitates the following degradation percentage estimation and RUL prediction tasks. The experimental results on a popular degradation data set suggest that the proposed method offers a novel perspective on data-driven prognostic studies and a promising tool for RUL estimations.

Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering

Remaining-useful-life prognostics for aircraft components are central for efficient and robust aircraft maintenance. In this paper, we propose an end-to-end approach to obtain online, model-based remaining-useful-life prognostics by learning from clusters of components with similar degradation trends. Time-series degradation measurements are first clustered using dynamic time-warping. For each cluster, a degradation model and a corresponding failure threshold are proposed. These cluster-specific degradation models, together with a particle filtering algorithm, are further used to obtain online remaining-useful-life prognostics. As a case study, we consider the operational data of several cooling units originating from a fleet of aircraft. The cooling units are clustered based on their degradation trends and remaining-useful-life prognostics are obtained in an online manner. In general, this approach provides support for intelligent aircraft maintenance where the analysis of cluster-specific component degradation models is integrated into the predictive maintenance process.

A Neural Network-Based Joint Prognostic Model for Data Fusion and Remaining Useful Life Prediction

With the rapid development of sensor and information technology, now multisensor data relating to the system degradation process are readily available for condition monitoring and remaining useful life (RUL) prediction. The traditional data fusion and RUL prediction methods are either not flexible enough to capture the highly nonlinear relationship between the health condition and the multisensor data or have not fully utilized the past observations to capture the degradation trajectory. In this article, we propose a joint prognostic model (JPM), where Bayesian linear models are developed for multisensor data, and an artificial neural network is proposed to model the nonlinear relationship between the residual life, the model parameters of each sensor data, and the observation epoch. A Bayesian updating scheme is developed to calculate the posterior distributions of the model parameters of each sensor data, which are further used to estimate the posterior predictive distributions of the residual life. The effectiveness and advantages of the proposed JPM are demonstrated using the commercial modular aero-propulsion system simulation data set.

Dynamics identification and forecasting of COVID-19 by switching Kalman filters

The COVID-19 pandemic has captivated scientific activity since its early days. Particular attention has been dedicated to the identification of underlying dynamics and prediction of future trend. In this work, a switching Kalman filter formalism is applied on dynamics learning and forecasting of the daily new cases of COVID-19. The main feature of this dynamical system is its ability to switch between different linear Gaussian models based on the observations and specified probabilities of transitions between these models. It is thus able to handle the problem of hidden state estimation and forecasting for models with non-Gaussian and nonlinear effects. The potential of this method is explored on the daily new cases of COVID-19 both at the state-level and the country-level in the US. The results suggest a common disease dynamics across states that share certain features. We also demonstrate the ability to make short to medium term predictions with quantifiable error bounds.

An Online Minimax Optimal Algorithm for Adversarial Multiarmed Bandit Problem

We investigate the adversarial multiarmed bandit problem and introduce an online algorithm that asymptotically achieves the performance of the best switching bandit arm selection strategy. Our algorithms are truly online such that we do not use the game length or the number of switches of the best arm selection strategy in their constructions. Our results are guaranteed to hold in an individual sequence manner, since we have no statistical assumptions on the bandit arm losses. Our regret bounds, i.e., our performance bounds with respect to the best bandit arm selection strategy, are minimax optimal up to logarithmic terms. We achieve the minimax optimal regret with computational complexity only log-linear in the game length. Thus, our algorithms can be efficiently used in applications involving big data. Through an extensive set of experiments involving synthetic and real data, we demonstrate significant performance gains achieved by the proposed algorithm with respect to the state-of-the-art switching bandit algorithms. We also introduce a general efficiently implementable bandit arm selection framework, which can be adapted to various applications.