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Lithium-Ion Battery Health Estimation with Incomplete Charging Data and Spiking ResNet
2024 Global Reliability and Prognostics and Health Management Conference (PHM-Beijing)
Year: 2024 | Conference Paper |
The accurate estimation of state of health (SOH) plays a crucial role in ensuring the safe operation of batteries. Most existing SOH estimation methods are based on complete charging or discharging data. However, in real-world applications, data are often derived from partial charging or discharging processes influenced by varying user habits, which brings great challenges to the accurate SOH esti...Show More
New Look at Bayesian Prognostic Methods
Jie Liu;Dong Wang;Jin-Zhen Kong;Naipeng Li;Zhike Peng;Kwok-Leung Tsui
IEEE Transactions on Automation Science and Engineering
Year: 2024 | Early Access Article |
Online remaining useful life (RUL) prediction is a core function of prognostics and health management (PHM), which provides solutions for comprehensive and personalised system management. RUL is realised by extrapolating timely updated prognostic models to reach a user-defined failure threshold. As of today, there are mainly two kinds of Bayesian prognostic methods. The first kind of Bayesian prog...Show More
New Look at Bayesian Prognostic Methods
Jie Liu;Dong Wang;Jin-Zhen Kong;Naipeng Li;Zhike Peng;Kwok-Leung Tsui
Year: 2024 | Early Access Article |
Multimodal Correlation-Aware Fusion Framework for Enhanced Machinery Health Prognosis With Unlabeled and Low-Quality Data Exploitation
IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Early Access Article |
Accurate machinery health prognosis, also known as remaining useful life (RUL) prediction, is critical for preventing catastrophic accidents and implementing predictive maintenance strategies. This makes it a highly attractive research area. Many existing studies have been developed on unimodal data, yet such data can only provide a restricted perspective and incomplete health state monitoring. So...Show More
Dynamic Vision-Based Machinery Fault Diagnosis with Cross-Modality Feature Alignment
Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades, and the vibration acceleration data collected by contact accelerometers have been widely investigated. In many industrial scenarios, contactless sensors are more preferred. The event camera is an emerging bio-inspired technology for vision sensing, which asynchronously records per-pixe...Show More
Self-data-driven Electromechanical Coupling Model Construction of Industrial Robots
2024 International Symposium on Intelligent Robotics and Systems (ISoIRS)
Year: 2024 | Conference Paper |
As a core component of industrial robots, the health state of RV reducers directly determines the operating accuracy and response speed of industrial robots. To ensure the reliability of robot operation, it is necessary to monitor the health state of RV reducers. Generally, the built-in sensors, such as encoders and current sensors, are integrated in the motor side of the industrial robot. Therefo...Show More
Graph Continual Learning Network: An Incremental Intelligent Diagnosis Method of Machines for New Fault Detection
IEEE Transactions on Automation Science and Engineering
Year: 2024 | Early Access Article |
Streaming data of machines is continuously collected in practical applications, which produces new fault information with respect to the health change. Therefore, a lifelong-learning intelligent diagnosis model is desired for new fault type recognition based on the streaming data. However, existing research in intelligent fault diagnosis always treats new fault type detection and class incremental...Show More
Intelligent Prototype Generation Method On Machine Fault Diagnosis Through Compressing Large Dataset
2024 7th International Symposium on Autonomous Systems (ISAS)
Year: 2024 | Conference Paper |
In modern industries, machine condition monitoring data have been available for improved maintenance. While big data generally benefits intelligent fault diagnosis performance, the significantly increased data amount inevitably poses high requirements for storage and computation. As a consequence, it is very difficult for the fault diagnosis model to be updated and applied efficiently. In order to...Show More
Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation
The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain. However, in engineering scenarios, achieving such high-quality label annotation is difficult and expensive. The incorrect label annotation produces two negative effects: 1) the complex decision boundary of diagnosis models lowers the generalization performan...Show More
An Optimal-Subdomain Generalization Method for Remaining Useful Life Prediction of Machinery Under Time-Varying Operation Conditions
Machinery often operates under time-varying conditions, which can lead to distribution discrepancies in degradation samples. However, most existing domain generalization-based methods for predicting remaining useful life (RUL) are applied to constant operation conditions, and they may demonstrate performance deteriorations across alternating operation conditions. This article proposes an optimal-s...Show More
Intelligent Machinery Fault Diagnosis With Event-Based Camera
Event-based cameras are the emerging bioinspired technology in vision sensing. Different from the traditional standard cameras, the event-based cameras asynchronously record the brightness change per pixel, and have the great merits of high temporal resolution, low energy consumption, high dynamic range, etc. While the event-based cameras have been initially exploited in several common vision-base...Show More
Health Prognostics of Multivariate Deteriorating Machinery Considering Unit-to-Unit Variability
2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou)
Year: 2023 | Conference Paper |
Multivariable deteriorating machinery (MDM) is commonly encountered in modern industrial environments, where its failure can be attributed to the gradual degradation of multiple health indicators (HIs). More and more researchers have directed their attention toward exploring the dependencies among these HIs and establishing multivariate degradation models. Nevertheless, existing methodologies have...Show More
Non-Contact Machine Vibration Sensing and Fault Diagnosis Method Based on Event Camera
2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Rotating machinery plays a key role in the field of industrial manufacturing and is an indispensable equipment in the production line. Many rotating machines work in extreme environments for a long time, which makes them more prone to various failures. Now it is popular to use the vibration signal collected by the accelerometer for fault diagnosis. However, accelerometers are contact sensors, and ...Show More
Machinery Health Prognostics With Multimodel Fusion Degradation Modeling
Degradation modeling aims to formulate the health state degradation process of machinery. Commonly used degradation models pay more attention to describing the global increasing or decreasing trend without considering the local fluctuation in the degradation process. To deal with the above-mentioned issue, this article proposes a multimodel fusion degradation modeling method. The basic idea is to ...Show More
Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks
In recent years, intelligent data-driven prognostic methods have been successfully developed, and good machinery health assessment performance has been achieved through explorations of data from multiple sensors. However, existing data-fusion prognostic approaches generally rely on the data availability of all sensors, and are vulnerable to potential sensor malfunctions, which are likely to occur ...Show More
A new FPCA-based remaining useful life prediction method for truncated degradation signals
2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2022 | Conference Paper |
Functional principal component analysis (FPCA) is a commonly used nonparametric degradation modeling technique. A basic requirement of FPCA is that different units must share the same scale in X-axis. A FPCA-based degradation modeling method has been specially designed for truncated degradation signals whose amplitudes are truncated at the failure threshold. This method formulates the degradation ...Show More
Subdomain Adaptation Transfer Learning Network for Fault Diagnosis of Roller Bearings
Due to the data distribution discrepancy, fault diagnosis models, trained with labeled data in one scene, likely fails in classifying by unlabeled data acquired from the other scenes. Transfer learning is capable to generalize successful application trained in one scene to the fault diagnosis in the other scenes. However, the existing transfer methods do not pay much attention to reduce adaptively...Show More
Remaining Useful Life Prediction Based on Multi-channel Attention Bidirectional Long Short-term Memory Network
2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Recently, a large number of deep learning-based prognostics methods for machinery remaining useful life (RUL) have been proposed. And massive monitoring data is the basis of deep learning-based RUL prediction methods. However, most existing methods usually assume that the monitoring data acquired from different sensors contain similar degradation information, and they lack consideration on effecti...Show More
Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems
Naipeng Li;Yaguo Lei;Nagi Gebraeel;Zhijian Wang;Xiao Cai;Pengcheng Xu;Biao Wang
Semi-observable systems are referred to as a kind of widely used industrial equipment whose physical degradation state is only observable via shutdown inspection. To monitor the degradation process of semi-observable systems online, different types of sensors are generally employed to collect monitoring signals. Lots of studies have been conducted to fuse multi-sensor signals to predict remaining ...Show More
Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery
To integrate the complete degradation information of machinery, deep learning-based prognostics approaches usually use monitoring data acquired by different sensors as the inputs of networks. These approaches, however, lack an explicit learning mechanism to effectively identify the distinctions of different sensor data and highlight the important degradation information, thereby affecting the accu...Show More
An Adaptable Cost-sensitive Imbalanced Fault Diagnosis Method Considering Sample-significance Information
2020 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Imbalanced datasets acquired from machinery are common in real cases and some cost-sensitive learning-based methods have been presented to tackle the imbalanced fault diagnosis in recent years. However, these methods manually assign misclassification costs that remain unchanged during training for each health condition and therefore they cannot be applied generally in different scenarios. Moreover...Show More
A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines
Deep transfer-learning-based diagnosis models are promising to apply diagnosis knowledge across related machines, but from which the collected data follow different distribution. To reduce the distribution discrepancy, Gaussian kernel induced maximum mean discrepancy (GK-MMD) is a widely used distance metric to impose constraints on the training of diagnosis models. However, the models using GK-MM...Show More
A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
IEEE Transactions on Reliability
Cited by: Papers (988)
Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions...Show More
Remaining Useful Life Prediction Based on Deep Residual Attention Network
2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
Deep learning is gaining growing interests in the field of remaining useful life (RUL) prediction and has achieved state-of-the-art results. Current deep learning-based prognostics approaches, however, do not consider the distinctions of different sensor data during representation learning, which affects their prediction accuracy and limits their generalization. To overcome this weakness, a new de...Show More
A Hybrid Transfer Learning Method for Fault Diagnosis of Machinery under Variable Operating Conditions
2019 Prognostics and System Health Management Conference (PHM-Qingdao)
Year: 2019 | Conference Paper |
Cited by: Papers (8)
Intelligent fault diagnosis has been a research hotspot in recent years. However, most of the works are conducted based on the hypothesis that training and testing data subject to the same distribution. In engineering scenarios, machines usually work under variable operation conditions, which results in the data from different conditions subject to distribution discrepancy. Since transfer learning...Show More
Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data
The success of intelligent fault diagnosis of machines relies on the following two conditions: 1) labeled data with fault information are available; and 2) the training and testing data are drawn from the same probability distribution. However, for some machines, it is difficult to obtain massive labeled data. Moreover, even though labeled data can be obtained from some machines, the intelligent f...Show More
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