With continuous improvements in integration, totalization and automation, remaining useful life predictions of mechanical equipment have become a key feature of technology and core element of equipment prediction and health management. The traditiona...
Remaining useful life prediction is one of the essential processes for machine system prognostics and health management. Although there are many new approaches based on deep learning for remaining useful life prediction emerging in recent years, thes...
Health state estimation can quantitatively evaluate the current degradation state of equipment, and remaining life prediction can quantitatively predict the remaining service time of equipment. These two technologies can provide a basis for condition...
This paper aims to establish a predictive model for battery lifetime using data analysis. The procedure of model establishment is illustrated in detail, including the data pre-processing, modeling, and prediction. The characteristics of lithium-ion b...
Aimed at the shortcomings of a single feature to characterize the health status and accurately predict the remaining life span of the equipment, a prediction method for a switch machine, based on the weighted Mahalanobis distance (WDMD), is proposed....
Aiming to resolve the problem of redundant information concerning rolling bearing degradation characteristics and to tackle the difficulty faced by convolutional deep learning models in learning feature information in complex time series, a predictio...
The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on th...
With the deepening adoption of whole-lifecycle management paradigms in aviation equipment, accurate prediction of remaining useful life has emerged as a pivotal technical enabler for ensuring flight safety and optimizing maintenance resource allocati...
The accurate prediction of airplane engine failure can provide a reasonable decision basis for airplane engine maintenance, effectively reducing maintenance costs and reducing the incidence of failure. According to the characteristics of the monitori...
With the development of smart manufacturing, in order to detect abnormal conditions of the equipment, a large number of sensors have been used to record the variables associated with production equipment. This study focuses on the prediction of Remai...
This article addresses the problem that the remaining useful life (RUL) prediction accuracy for a high-speed rail catenary is not accurate enough, leading to costly and time-consuming periodic planned and reactive maintenance costs. A new method for...
In the operation of a power distribution system, miniature circuit breakers (MCBs) are subjected to the synergistic effect of electrical and mechanical stresses in service, and their operational performance is progressively degraded, which is prone t...
Turbofan engines are known as the heart of the aircraft. The turbofan’s health state determines the aircraft’s operational status. Therefore, the equipment monitoring and maintenance of the engine is an important part of ensuring the heal...
This paper aims to address the challenges of significant data distribution differences and extreme data imbalances in the remaining useful life prediction modeling of rotating components of complex intelligent equipment under various working conditio...
Remanufacturing is an activity of the circular economy model whose purpose is to keep the high value of products and materials. As opposed to the currently employed linear economic model, remanufacturing targets the extension of products and reduces...
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 pr...
Remaining useful life prediction can assess the time to failure of degradation systems. Currently, numerous neural network-based prediction methods have been proposed by researchers. However, most of the work contains an implicit prerequisite: the ne...
In recent years, deep learning has become prevalent in Remaining Useful-Life (RUL) prediction of bearings. The current deep-learning-based RUL methods tend to extract high dimensional features from the original vibration data to construct the Health...
Precise prediction of the remaining useful life (RUL) of rolling bearings is crucial for ensuring the smooth functioning of machinery and minimizing maintenance costs. The time-domain features can reflect the degenerative state of the bearings and re...
Data imbalance and large data probability distribution discrepancies are major factors that reduce the accuracy of remaining useful life (RUL) prediction of high-reliability rotating machinery. In feature extraction, most deep transfer learning model...
Remaining Useful Life (RUL) prediction is a key factor in fault diagnosis, prediction, and health management (PHM) during equipment operation and service. Its purpose is to predict the time interval from the current moment to the complete failure of...
The whole life cycle degradation data set of rolling bearings has the characteristics of large capacity, diversity, and non-stationarity. As a powerful tool for processing such time series data in deep learning algorithms, LSTM is prone to the loss o...
Objectives: To address the difficulty of data sharing under privacy constraints and the performance degradation of conventional federated models caused by pronounced inter-client data heterogeneity in rolling bearing remaining useful life prediction,...
To advance the prediction accuracy of remaining useful life (RUL) for rolling element bearings, this study proposes a novel hybrid prognostic framework that synergistically integrates digital twin (DT) technology with the Time-series Mixer (TSMixer)...
Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address...
Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration a...
For safe maintenance and to reduce the risk of mechanical faults, the remaining useful life (RUL) estimate of bearings is significant. The typical methods of bearings’ RUL prediction suffer from low prediction accuracy because of the difficulty...
Fatigue failure is a typical failure mode of welded structures. It is of great engineering significance to predict the remaining fatigue life of structures after a certain period of service. In this paper, a two-stage hybrid deep learning approach is...
Prognostics and health management (PHM) are gradually being applied to production management processes as industrial production is gradually undergoing a transformation, turning into intelligent production and leading to increased demands on the reli...
Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for...
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is a prerequisite for reliable battery management, predictive maintenance and safe second-life utilization. Existing approaches fall into two families. Physics-based mode...
Accurate remaining useful life (RUL) prediction of rolling bearings was essential for condition-based maintenance because bearing service degradation was primarily governed by progressive rolling-contact fatigue at the rollingelement–raceway in...
An efficient Remaining Useful Life (RUL) prediction method is one of the most important features of a condition-based maintenance system. A running machine’s RUL prognosis in its real-time is a challenging task, especially when there is no hist...
Remaining useful life (RUL) prediction is a key enabler for predictive maintenance. In fact, the possibility of accurately and reliably predicting the RUL of a system, based on a record of its monitoring data, can allow users to schedule maintenance...
Remaining useful life (RUL) prediction of equipment has important significance for guaranteeing production efficiency, reducing maintenance cost, and improving plant safety. This paper proposes a novel method based on an new particle filter (PF) for...
Predictive maintenance, recognized as an effective health management strategy for extending the lifetime of devices, has emerged as a hot research topic in recent years. A general method is to execute two separate steps: data-driven remaining useful...
The DC circuit breaker is a crucial equipment for eliminating faults in DC transmission lines. The electromagnetic release functions as a critical component that restricts the circuit breaker’s lifespan. It is essential to prioritize its safety...
Rolling bearings are one of the most important and indispensable components of a mechanical system, and an accurate prediction of their remaining life is essential to ensuring the reliable operation of a mechanical system. In order to effectively uti...
This paper provides a comprehensive review of recent advances in remaining useful life prediction for lithium-ion battery energy storage systems. Existing approaches are generally categorized into model-based methods, data-driven methods, and hybrid...
Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just in time learning (JITL) method was proposed to solve t...
The solid oxide fuel cell (SOFC) system is complicated because the characteristics of gas, heat, and electricity are intricately coupled. During the operation of the system, problems such as frequent failures and a decrease in the stack’s perfo...
Wind-powered electricity generation has grown significantly over the past decade. While there are many components that might impact their useful life, the gearbox and generator bearings are among the most fragile components in wind turbines. Therefor...
With the widespread application of unmanned aerial vehicles (UAVs) across various domains, the reliability and lifespan prediction of their core power units—the engines—has become a critical research focus. This study addresses the degrad...
Remaining Useful Life (RUL) prediction is an important component of failure prediction and health management (PHM). Current life prediction studies require large amounts of tagged training data assuming that the training data and the test data follow...
Remaining useful life (RUL) prediction is critical for improving reliability and supporting predictive maintenance in aero-engine systems. However, existing methods have limitations in jointly modeling the spatial correlations between multi-sensor si...
It is difficult to accurately extract the health index of non-stationary signals of rolling bearings under variable rotational speed, which also leads to greater prediction error for bearing degradation models with fixed parameters. For this reason,...
Remaining useful life (RUL) prediction of rolling bearings is essential for ensuring the safe operation and condition-based maintenance of rotating machinery. To address unreliable degradation-onset identification and insufficient joint modeling of t...
As the core component of aircraft systems, aeroengines require accurate Remaining Useful Life (RUL) prediction to ensure flight safety, which serves as a key part of Prognostics and Health Management (PHM). Traditional RUL prediction methods primaril...
Remaining useful life (RUL) prediction is a core technology in prognostics and health management (PHM), crucial for ensuring the safe and efficient operation of modern industrial systems. Although deep learning methods have shown potential in RUL pre...
Lithium-ion (Li-ion) batteries have become essential in modern industries and domestic applications due to their high energy density and efficiency. However, they experience gradual degradation over time, which presents significant challenges in main...
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