1
|
Gao Y, Ahmad Z, Kim JM. The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder. SENSORS (BASEL, SWITZERLAND) 2024; 24:2382. [PMID: 38675999 PMCID: PMC11054358 DOI: 10.3390/s24082382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/06/2024] [Accepted: 04/07/2024] [Indexed: 04/28/2024]
Abstract
The prediction of the remaining useful life (RUL) is important for the conditions of rotating machinery to maintain reliability and decrease losses. This study proposes an efficient approach based on an adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD) and a convolutional LSTM autoencoder to achieve the feature extraction, health index analysis, and RUL prediction for rotating machinery. First, the ACYCBD is used to filter noise from the vibration signals. Second, based on the peak value properties, a novel health index (HI) is designed to analyze the health conditions for the denoising signal, showing a high sensitivity for the degradation of bearings. Finally, for better prognostics and health management of the rotating machinery, based on convolutional layers and LSTM, an autoencoder can achieve a transform convolutional LSTM network to develop a convolutional LSTM autoencoder (ALSTM) model that can be applied to forecast the health trend for rotating machinery. Compared with the SVM, CNN, LSTM, GRU, and DTGRU methods, our experiments demonstrate that the proposed approach has the greatest performance for the prediction of the remaining useful life of rotating machinery.
Collapse
Affiliation(s)
| | | | - Jong-Myon Kim
- Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea; (Y.G.); (Z.A.)
| |
Collapse
|
2
|
Wu J, Kong L, Kang S, Zuo H, Yang Y, Cheng Z. Aircraft Engine Fault Diagnosis Model Based on 1DCNN-BiLSTM with CBAM. SENSORS (BASEL, SWITZERLAND) 2024; 24:780. [PMID: 38339497 PMCID: PMC10857147 DOI: 10.3390/s24030780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024]
Abstract
As the operational status of aircraft engines evolves, their fault modes also undergo changes. In response to the operational degradation trend of aircraft engines, this paper proposes an aircraft engine fault diagnosis model based on 1DCNN-BiLSTM with CBAM. The model can be directly applied to raw monitoring data without the need for additional algorithms to extract fault degradation features. It fully leverages the advantages of 1DCNN in extracting local features along the spatial dimension and incorporates CBAM, a channel and spatial attention mechanism. CBAM could assign higher weights to features relevant to fault categories and make the model pay more attention to them. Subsequently, it utilizes BiLSTM to handle nonlinear time feature sequences and bidirectional contextual feature information. Finally, experimental validation is conducted on the publicly available CMAPSS dataset from NASA, categorizing fault modes into three types: faultless, HPC fault (the single fault), and HPC&Fan fault (the mixed fault). Comparative analysis with other models reveals that the proposed model has a higher classification accuracy, which is of practical significance in improving the reliability of aircraft engine operations and for Remaining Useful Life (RUL) prediction.
Collapse
Affiliation(s)
- Jiaju Wu
- Institute of Computer Application China Academy of Engineering Physics, Mianyang 621999, China
- College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Linggang Kong
- Institute of Computer Application China Academy of Engineering Physics, Mianyang 621999, China
| | - Shijia Kang
- Institute of Computer Application China Academy of Engineering Physics, Mianyang 621999, China
| | - Hongfu Zuo
- College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Yonghui Yang
- Institute of Computer Application China Academy of Engineering Physics, Mianyang 621999, China
| | - Zheng Cheng
- Institute of Computer Application China Academy of Engineering Physics, Mianyang 621999, China
| |
Collapse
|
3
|
Liu Q, Deng W, Pham DT, Hu J, Wang Y, Zhou Z. A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model. MICROMACHINES 2023; 14:mi14050946. [PMID: 37241570 DOI: 10.3390/mi14050946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023]
Abstract
For remanufacturing to be more economically attractive, there is a need to develop automatic disassembly and automated visual detection methods. Screw removal is a common step in end-of-life product disassembly for remanufacturing. This paper presents a two-stage detection framework for structurally damaged screws and a linear regression model of reflection features that allows the detection framework to be conducted under uneven illumination conditions. The first stage employs reflection features to extract screws together with the reflection feature regression model. The second stage uses texture features to filter out false areas that have reflection features similar to those of screws. A self-optimisation strategy and weighted fusion are employed to connect the two stages. The detection framework was implemented on a robotic platform designed for disassembling electric vehicle batteries. This method allows screw removal to be conducted automatically in complex disassembly tasks, and the utilisation of the reflection feature and data learning provides new ideas for further research.
Collapse
Affiliation(s)
- Quan Liu
- School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
| | - Wupeng Deng
- School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
- Department of Mechanical Engineering, University of Birmingham, Birmingham B15 2TT, UK
| | - Duc Truong Pham
- Department of Mechanical Engineering, University of Birmingham, Birmingham B15 2TT, UK
| | - Jiwei Hu
- School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
| | - Yongjing Wang
- Department of Mechanical Engineering, University of Birmingham, Birmingham B15 2TT, UK
| | - Zude Zhou
- School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
| |
Collapse
|
4
|
Xu Z, Yang Y, Gao X, Hu M. DCFF-MTAD: A Multivariate Time-Series Anomaly Detection Model Based on Dual-Channel Feature Fusion. SENSORS (BASEL, SWITZERLAND) 2023; 23:3910. [PMID: 37112251 PMCID: PMC10142265 DOI: 10.3390/s23083910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/09/2023] [Accepted: 04/10/2023] [Indexed: 06/19/2023]
Abstract
The detection of anomalies in multivariate time-series data is becoming increasingly important in the automated and continuous monitoring of complex systems and devices due to the rapid increase in data volume and dimension. To address this challenge, we present a multivariate time-series anomaly detection model based on a dual-channel feature extraction module. The module focuses on the spatial and time features of the multivariate data using spatial short-time Fourier transform (STFT) and a graph attention network, respectively. The two features are then fused to significantly improve the model's anomaly detection performance. In addition, the model incorporates the Huber loss function to enhance its robustness. A comparative study of the proposed model with existing state-of-the-art ones was presented to prove the effectiveness of the proposed model on three public datasets. Furthermore, by using in shield tunneling applications, we verify the effectiveness and practicality of the model.
Collapse
Affiliation(s)
- Zheng Xu
- SHU-SUCG Research Centre of Building Information, Shanghai University, Shanghai 201400, China
- SILC Business School, Shanghai University, Shanghai 201800, China
| | - Yumeng Yang
- SHU-SUCG Research Centre of Building Information, Shanghai University, Shanghai 201400, China
- SILC Business School, Shanghai University, Shanghai 201800, China
| | - Xinwen Gao
- SHU-SUCG Research Centre of Building Information, Shanghai University, Shanghai 201400, China
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| | - Min Hu
- SHU-SUCG Research Centre of Building Information, Shanghai University, Shanghai 201400, China
- SILC Business School, Shanghai University, Shanghai 201800, China
| |
Collapse
|
5
|
Shan G, Li G, Wang Y, Xing C, Zheng Y, Yang Y. Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems. MICROMACHINES 2023; 14:344. [PMID: 36838043 PMCID: PMC9958958 DOI: 10.3390/mi14020344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/26/2023] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Abstract
Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and other fields, and signal integrity (SI) determines their performance. Establishing accurate and fast predictive models and intelligent optimization models for SI in microsystems is extremely essential. Recently, neural networks (NNs) and heuristic optimization algorithms have been widely used to predict the SI performance of microsystems. This paper systematically summarizes the neural network methods applied in the prediction of microsystem SI performance, including artificial neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), convolutional neural network (CNN), etc., as well as intelligent algorithms applied in the optimization of microsystem SI, including genetic algorithm (GA), differential evolution (DE), deep partition tree Bayesian optimization (DPTBO), two stage Bayesian optimization (TSBO), etc., and compares and discusses the characteristics and application fields of the current applied methods. The future development prospects are also predicted. Finally, the article is summarized.
Collapse
Affiliation(s)
- Guangbao Shan
- School of Microelectronics, Xidian University, Xi’an 710071, China
| | - Guoliang Li
- School of Microelectronics, Xidian University, Xi’an 710071, China
| | - Yuxuan Wang
- School of Microelectronics, Xidian University, Xi’an 710071, China
| | - Chaoyang Xing
- Beijing Institute of Aerospace Control Devices, Beijing 100039, China
| | - Yanwen Zheng
- School of Microelectronics, Xidian University, Xi’an 710071, China
| | - Yintang Yang
- School of Microelectronics, Xidian University, Xi’an 710071, China
| |
Collapse
|
6
|
Mey O, Neufeld D. Explainable AI Algorithms for Vibration Data-Based Fault Detection: Use Case-Adadpted Methods and Critical Evaluation. SENSORS (BASEL, SWITZERLAND) 2022; 22:9037. [PMID: 36501736 PMCID: PMC9736871 DOI: 10.3390/s22239037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/10/2022] [Accepted: 11/16/2022] [Indexed: 06/17/2023]
Abstract
Analyzing vibration data using deep neural networks is an effective way to detect damages in rotating machinery at an early stage. However, the black-box approach of these methods often does not provide a satisfactory solution because the cause of classifications is not comprehensible to humans. Therefore, this work investigates the application of the explainable AI (XAI) algorithms to convolutional neural networks for vibration-based condition monitoring. Thus, the three XAI algorithms GradCAM, LRP and LIME with a modified perturbation strategy are applied to classifications based on the Fourier transform as well as the order analysis of the vibration signal. The following visualization as frequency-RPM maps and order-RPM maps allows for an effective assessment of saliency values for variable periodicity of the data, which translates to a varying rotation speed of a real-world machine. To compare the explanatory power of the XAI methods, investigations are first carried out with a synthetic data set with known class-specific characteristics. Both a visual and a quantitative analysis of the resulting saliency maps are presented. Then, a real-world data set for vibration-based imbalance classification on an electric motor, which runs at a broad range of rotation speeds, is used. The results indicate that the investigated algorithms are each partially successful in providing sample-specific saliency maps which highlight class-specific features and omit features which are not relevant for classification.
Collapse
Affiliation(s)
- Oliver Mey
- Fraunhofer IIS/EAS, Fraunhofer Institute for Integrated Circuits, Division Engineering of Adaptive Systems, 01187 Dresden, Germany
| | - Deniz Neufeld
- Cognitive Systems Group, University of Bamberg, 96050 Bamberg, Germany
| |
Collapse
|