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Fault Diagnosis and Prognosis in Rotating Machines

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 12 July 2024 | Viewed by 11342

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Special Issue Editor

Dr. Zheming Tong

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Guest Editor

School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: intelligent sensors; human-computer interaction; HVAC systems; mechanical design; air pollutant tracking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rotating machines are essentially composed of rotating parts, covering a broad range of mechanical applications. As they often operate under harsh conditions, the health state of rotating machines plays a critical role in affecting the performance of the overall mechanical system. In recent years, the fault diagnosis and prognosis of rotating machines has attracted considerable attention because such components often experience unexpected downtime and failures. Various diagnostic methods have been proposed for different types of rotating machines, such as those based on vibration signals, high-speed imaging, acoustic emissions, etc.

This Special Issue will collect original research and review articles on recent findings in the areas of fault diagnosis and prognosis in rotating machines, especially with advanced data-driven techniques. Authors are asked to declare their research objectives, state all the assumptions used to derive new models, and clearly define their research hypotheses.

Dr. Zheming Tong
Guest Editor

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Keywords

rotating machines
data-driven methods
fault diagnosis and prognosis
high-speed imaging
machine learning
signal processing

Published Papers (7 papers)

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Research

18 pages, 4681 KiB

Open AccessArticle

Bearing Fault Diagnosis via Stepwise Sparse Regularization with an Adaptive Sparse Dictionary

by Lichao Yu, Chenglong Wang, Fanghong Zhang and Huageng Luo

Sensors 2024, 24(8), 2445; https://doi.org/10.3390/s24082445 - 11 Apr 2024

Viewed by 281

Abstract

Vibration monitoring is one of the most effective approaches for bearing fault diagnosis. Within this category of techniques, sparsity constraint-based regularization has received considerable attention for its capability to accurately extract repetitive transients from noisy vibration signals. The optimal solution of a sparse regularization problem is determined by the regularization term and the data fitting term in the cost function according to their weights, so a tradeoff between sparsity and data fidelity has to be made inevitably, which restricts conventional regularization methods from maintaining strong sparsity-promoting capability and high fitting accuracy at the same time. To address the limitation, a stepwise sparse regularization (SSR) method with an adaptive sparse dictionary is proposed. In this method, the bearing fault diagnosis is modeled as a multi-parameter optimization problem, including time indexes of the sparse dictionary and sparse coefficients. Firstly, sparsity-enhanced optimization is conducted by amplifying the regularization parameter, making the time indexes and the number of atoms adaptively converge to the moments when impulses occur and the number of impulses, respectively. Then, fidelity-enhanced optimization is carried out by removing the regularization term, thereby obtaining the high-precision reconstruction amplitudes. Simulations and experiments verify that the reconstruction accuracy of the SSR method outperforms other sparse regularization methods under most noise conditions, and thus the proposed method can provide more accurate results for bearing fault diagnosis. Full article

(This article belongs to the Special Issue Fault Diagnosis and Prognosis in Rotating Machines)

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13 pages, 4537 KiB

Open AccessCommunication

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

by Yangde Gao, Zahoor Ahmad and Jong-Myon Kim

Sensors 2024, 24(8), 2382; https://doi.org/10.3390/s24082382 - 09 Apr 2024

Viewed by 312

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. Full article

(This article belongs to the Special Issue Fault Diagnosis and Prognosis in Rotating Machines)

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22 pages, 6049 KiB

Open AccessArticle

Weighted Domain Adaptation Using the Graph-Structured Dataset Representation for Machinery Fault Diagnosis under Varying Operating Conditions

by Junhyuk Choi, Dohyeon Kong and Hyunbo Cho

Sensors 2024, 24(1), 188; https://doi.org/10.3390/s24010188 - 28 Dec 2023

Viewed by 629

Abstract

Data-driven fault diagnosis has received significant attention in the era of big data. Most data-driven methods have been developed under the assumption that both training and test data come from identical data distributions. However, in real-world industrial scenarios, data distribution often changes due to varying operating conditions, leading to a degradation of diagnostic performance. Although several domain adaptation methods have shown their feasibility, existing methods have overlooked metadata from the manufacturing process and treated all domains uniformly. To address these limitations, this article proposes a weighted domain adaptation method using a graph-structured dataset representation. Our framework involves encoding a collection of datasets into the proposed graph structure, which captures relations between datasets based on metadata and raw data simultaneously. Then, transferability scores of candidate source datasets for a target are estimated using the constructed graph and a graph embedding model. Finally, the fault diagnosis model is established with a voting ensemble of the base classifiers trained on candidate source datasets and their estimated transferability scores. For validation, two case studies on rotor machinery, specifically tool wear and bearing fault detection, were conducted. The experimental results demonstrate the effectiveness and superiority of the proposed method over other existing domain adaptation methods. Full article

(This article belongs to the Special Issue Fault Diagnosis and Prognosis in Rotating Machines)

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16 pages, 6783 KiB

Open AccessArticle

Acoustic-Based Rolling Bearing Fault Diagnosis Using a Co-Prime Circular Microphone Array

by Chi Li, Changzheng Chen and Xiaojiao Gu

Sensors 2023, 23(6), 3050; https://doi.org/10.3390/s23063050 - 12 Mar 2023

Cited by 1 | Viewed by 1596

Abstract

This study proposes a high-efficiency method using a co-prime circular microphone array (CPCMA) for the bearing fault diagnosis, and discusses the acoustic characteristics of three fault-type signals at different rotation speeds. Due to the close positions of various bearing components, radiation sounds are seriously mixed, and it is challenging to separate the fault features. Direction-of-arrival (DOA) estimation can be used to suppress noise and directionally enhance sound sources of interest; however, classical array configurations usually require a large number of microphones to achieve high accuracy. To address this, a CPCMA is introduced to raise the array’s degrees of freedom in order to reduce the dependence on the microphone numbers and computation complexity. The estimation of signal parameters via rotational invariance techniques (ESPRIT) applied to a CPCMA can quickly figure out the DOA estimation without any prior knowledge. By using the techniques above, a sound source motion-tracking diagnosis method is proposed according to the movement characteristics of impact sound sources for each fault type. Additionally, more precise frequency spectra are obtained, which are used in combination to determine the fault types and locations. Full article

(This article belongs to the Special Issue Fault Diagnosis and Prognosis in Rotating Machines)

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18 pages, 7138 KiB

Open AccessArticle

Remaining Useful Life Prediction Based on Adaptive SHRINKAGE Processing and Temporal Convolutional Network

by Haitao Wang, Jie Yang, Lichen Shi and Ruihua Wang

Sensors 2022, 22(23), 9088; https://doi.org/10.3390/s22239088 - 23 Nov 2022

Cited by 3 | Viewed by 1701

Abstract

The remaining useful life (RUL) prediction is important for improving the safety, supportability, maintainability, and reliability of modern industrial equipment. The traditional data-driven rolling bearing RUL prediction methods require a substantial amount of prior knowledge to extract degraded features. A large number of recurrent neural networks (RNNs) have been applied to RUL, but their shortcomings of long-term dependence and inability to remember long-term historical information can result in low RUL prediction accuracy. To address this limitation, this paper proposes an RUL prediction method based on adaptive shrinkage processing and a temporal convolutional network (TCN). In the proposed method, instead of performing the feature extraction to preprocess the original data, the multi-channel data are directly used as an input of a prediction network. In addition, an adaptive shrinkage processing sub-network is designed to allocate the parameters of the soft-thresholding function adaptively to reduce noise-related information amount while retaining useful features. Therefore, compared with the existing RUL prediction methods, the proposed method can more accurately describe RUL based on the original historical data. Through experiments on a PHM2012 rolling bearing data set, a XJTU-SY data set and comparison with different methods, the predicted mean absolute error (MAE) is reduced by 52% at most, and the root mean square error (RMSE) is reduced by 64% at most. The experimental results show that the proposed adaptive shrinkage processing method, combined with the TCN model, can predict the RUL accurately and has a high application value. Full article

(This article belongs to the Special Issue Fault Diagnosis and Prognosis in Rotating Machines)

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15 pages, 3416 KiB

Open AccessArticle

Balancing of Motor Armature Based on LSTM-ZPF Signal Processing

by Ruiwen Dong, Mengxuan Li, Ao Sun, Zhenrong Lu, Dong Jiang and Weiyu Chen

Sensors 2022, 22(23), 9043; https://doi.org/10.3390/s22239043 - 22 Nov 2022

Cited by 1 | Viewed by 4171

Abstract

Signal processing is important in the balancing of the motor armature, where the balancing accuracy depends on the extraction of the signal amplitude and phase from the raw vibration signal. In this study, a motor armature dynamic balancing method based on the long short-term memory network (LSTM) and zero-phase filter (ZPF) is proposed. This method mainly focuses on the extraction accuracy of amplitude and phase from unbalanced signals of the motor armature. The ZPF is used to accurately extract the phase, while the LSTM network is trained to extract the amplitude. The proposed method combines the advantages of both methods, whereby the problems of phase shift and amplitude loss when used alone are solved, and the motor armature unbalance signal is accurately obtained. The unbalanced mass and phase are calculated using the influence coefficient method. The effectiveness of the proposed method is proven using the simulated motor armature vibration signal, and an experimental investigation is undertaken to verify the dynamic balancing method. Two amplitude evaluation metrics and three phase evaluation metrics are proposed to judge the extraction accuracy of the amplitude and phase, whereas amplitude and frequency spectrum analysis are used to judge the dynamic balancing results. The results illustrate that the proposed method has higher dynamic balancing accuracy. Moreover, it has better extraction accuracy for the amplitude and phase of unbalanced signals compared with other methods, and it has good anti-noise performance. The determination coefficient of the amplitude is 0.9999, and the average absolute error of the phase is 2.4°. The proposed method considers both fidelity and denoising, which ensuring the accuracy of armature dynamic balancing. Full article

(This article belongs to the Special Issue Fault Diagnosis and Prognosis in Rotating Machines)

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18 pages, 10865 KiB

Open AccessArticle

Compound Fault Feature Extraction of Rolling Bearing Acoustic Signals Based on AVMD-IMVO-MCKD

by Shishuai Wu, Jun Zhou and Tao Liu

Sensors 2022, 22(18), 6769; https://doi.org/10.3390/s22186769 - 07 Sep 2022

Cited by 6 | Viewed by 1656

Abstract

The compound fault acoustic signal of a rolling bearing has the characteristics of a varying noise mixture, a low signal-to-noise ratio (SNR), and nonlinearity, which makes it difficult to separate and extract exactly the fault features of compound fault signals. A fault feature extraction approach combining adaptive variational modal decomposition (AVMD) and improved multiverse optimization (IMVO) algorithm parameterized maximum correlated kurtosis deconvolution (MCKD)—named AVMD-IMVO-MCKD—is proposed. In order to adaptively select the parameters of VMD and MCKD, an adaptive optimization method of VMD is proposed, and an improved multiverse optimization (IMVO) algorithm is proposed to determine the parameters of MCKD. Firstly, the acoustic signal of bearing compound faults is decomposed by AVMD to generate several modal components, and the optimal modal component is selected as the reconstruction signal depending on the minimum information entropy of the modal components. Secondly, IMVO is utilized to select the parameters of MCKD, and then MCKD processing is performed on the reconstructed signal. Finally, the compound fault features of the bearing are extracted by the envelope spectrum. Both simulation analysis and acoustic signal experimental data analysis show that the proposed approach can efficiently extract the acoustic signal fault features of bearing compound faults. Full article

(This article belongs to the Special Issue Fault Diagnosis and Prognosis in Rotating Machines)

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