Research

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18 pages, 4251 KiB

Open AccessArticle

Experimental Vibration Data in Fault Diagnosis: A Machine Learning Approach to Robust Classification of Rotor and Bearing Defects in Rotating Machines

by Khalid M. Almutairi and Jyoti K. Sinha

Machines 2023, 11(10), 943; https://doi.org/10.3390/machines11100943 - 05 Oct 2023

Cited by 1 | Viewed by 1472

Abstract

This study builds upon previous research that utilised a vibration-based machine learning (VML) approach for diagnosing rotor-related faults in rotating machinery. The original method used artificial neural networks (ANN) to classify rotor-related faults based on optimised vibration parameters from the time and frequency [...] Read more.

This study builds upon previous research that utilised a vibration-based machine learning (VML) approach for diagnosing rotor-related faults in rotating machinery. The original method used artificial neural networks (ANN) to classify rotor-related faults based on optimised vibration parameters from the time and frequency domains. This study expands the application of this vibration-based machine learning approach to include the anti-friction bearing faults in addition to the rotor faults. The earlier suggested vibration-based parameters, both in time and frequency domains, are further revised to accommodate bearing-related defects. The study utilises the measured vibration data from a laboratory-scale rotating test rig with different experimentally simulated faults in the rotor and bearings. The proposed VML model is developed for both rotor and bearing defects at a rotor speed that is above the first critical speed. To gauge the robustness of the proposed VML model, it is further tested at two different rotating speeds, one below the first critical speed and the other above the second critical speed. The paper presents the methodology, the rig and measured vibration data, the optimised parameters, and the findings. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Fault Diagnosis and Prognosis)

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15 pages, 4446 KiB

Open AccessArticle

LSTM-Based Condition Monitoring and Fault Prognostics of Rolling Element Bearings Using Raw Vibrational Data

by Yasir Saleem Afridi, Laiq Hasan, Rehmat Ullah, Zahoor Ahmad and Jong-Myon Kim

Machines 2023, 11(5), 531; https://doi.org/10.3390/machines11050531 - 06 May 2023

Cited by 6 | Viewed by 1997

Abstract

The 4.0 industry revolution and the prevailing technological advancements have made industrial units more intricate. These complex electro-mechanical units now aim to improve efficiency and increase reliability. Downtime of such essential units in the current competitive age is unaffordable. The paradigm of fault [...] Read more.

The 4.0 industry revolution and the prevailing technological advancements have made industrial units more intricate. These complex electro-mechanical units now aim to improve efficiency and increase reliability. Downtime of such essential units in the current competitive age is unaffordable. The paradigm of fault diagnostics is being shifted from conventional to proactive predictive approaches. As a result, Condition-based Monitoring and prognostics are now essential components of complex industrial systems. This research is focused on developing a fault prognostic system using Long Short-Term Memory for rolling element bearings because they are a critical component of industrial systems and have one of the highest fault frequencies. Compared to other research, feature engineering is minimized by using raw time series sensor data as an input to the model. Our model achieved the lowest root mean square error and outperformed similar research models where time domain, frequency domain, or time-frequency domain features were used as input to the model. Furthermore, using raw vibration data also enabled better generalization of the model. This has been confirmed by evaluating the performance of the developed model against vibration data generated by distinct sources, including hydro and wind power turbines. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Fault Diagnosis and Prognosis)

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24 pages, 5597 KiB

Open AccessArticle

Euler Representation-Based Structural Balance Discriminant Projection for Machinery Fault Diagnosis

by Maoyan Zhang, Yanmin Zhu, Shuzhi Su, Xianjin Fang and Ting Wang

Machines 2023, 11(2), 307; https://doi.org/10.3390/machines11020307 - 19 Feb 2023

Cited by 1 | Viewed by 1040

Abstract

Fault diagnosis methods are usually sensitive to outliers and it is difficult to obtain and balance global and local discriminant information, which leads to poor separation between classes of low-dimensional discriminant features. For this problem, we propose an Euler representation-based structural balance discriminant [...] Read more.

Fault diagnosis methods are usually sensitive to outliers and it is difficult to obtain and balance global and local discriminant information, which leads to poor separation between classes of low-dimensional discriminant features. For this problem, we propose an Euler representation-based structural balance discriminant projection (ESBDP) algorithm for rotating machine fault diagnosis. First, the method maps the high-dimensional fault features into the Euler representation space through the cosine metric to expand the differences between heterogeneous fault samples while reducing the impact on outliers. Then, four objective functions with different structure and class information are constructed in this space. On the basis of fully mining the geometric structure information of fault data, the local intra-class aggregation and global inter-class separability of the low-dimensional discriminative features are further improved. Finally, we provide an adaptive balance strategy for constructing a unified optimization model of ESBDP, which achieves the elastic balance between global and local features in the projection subspace. The diagnosis performance of the ESBDP algorithm is explored by two machinery fault cases of bearing and gearbox. Encouraging experimental results show that the algorithm can capture effective fault discriminative features and can improve the accuracy of fault diagnosis. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Fault Diagnosis and Prognosis)

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16 pages, 3773 KiB

Open AccessArticle

Multi-Scale Recursive Semi-Supervised Deep Learning Fault Diagnosis Method with Attention Gate

by Shanjie Tang, Chaoge Wang, Funa Zhou, Xiong Hu and Tianzhen Wang

Machines 2023, 11(2), 153; https://doi.org/10.3390/machines11020153 - 23 Jan 2023

Cited by 2 | Viewed by 1394

Abstract

The efficiency of deep learning-based fault diagnosis methods for bearings is affected by the sample size of the labeled data, which might be insufficient in the engineering field. Self-training is a commonly used semi-supervised method, which is usually limited by the accuracy of [...] Read more.

The efficiency of deep learning-based fault diagnosis methods for bearings is affected by the sample size of the labeled data, which might be insufficient in the engineering field. Self-training is a commonly used semi-supervised method, which is usually limited by the accuracy of features for unlabeled data screening. It is significant to design an efficient training mechanism to extract accurate features and a novel feature fusion mechanism to ensure that the fused feature is capable of screening. A novel training mechanism of multi-scale recursion (MRAE) is designed for Autoencoder in this article, which can be used for accurate feature extraction with a small amount of labeled data. An attention gate-based fusion mechanism was constructed to make full use of all useful features in the sense that it can incorporate distinguishing features on different scales. Utilizing large numbers of unlabeled data, the proposed multi-scale recursive semi-supervised deep learning fault diagnosis method with attention gate (MRAE-AG) can efficiently improve the fault diagnosis performance of DNNs trained by a small number of labeled data. A benchmark dataset from the Case Western Reserve University bearing data center was used to validate this novel method which shows that 7.76% accuracy improvement can be achieved in the case when only 10 labeled samples was available for supervised training of the DNN-based fault diagnosis model. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Fault Diagnosis and Prognosis)

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17 pages, 5805 KiB

Open AccessArticle

Refined Composite Multiscale Fluctuation Dispersion Entropy and Supervised Manifold Mapping for Planetary Gearbox Fault Diagnosis

by Haocheng Su, Zhenya Wang, Yuxiang Cai, Jiaxin Ding, Xinglong Wang and Ligang Yao

Machines 2023, 11(1), 47; https://doi.org/10.3390/machines11010047 - 01 Jan 2023

Cited by 3 | Viewed by 1491

Abstract

A novel fault diagnosis scheme was developed to address the difficulty of feature extraction for planetary gearboxes using refined composite multiscale fluctuation dispersion entropy (RCMFDE) and supervised manifold mapping. The RCMFDE was first utilized in this scheme to fully mine fault features from [...] Read more.

A novel fault diagnosis scheme was developed to address the difficulty of feature extraction for planetary gearboxes using refined composite multiscale fluctuation dispersion entropy (RCMFDE) and supervised manifold mapping. The RCMFDE was first utilized in this scheme to fully mine fault features from planetary gearbox signals under multiple scales. Subsequently, as a supervised manifold mapping method, supervised isometric mapping (S-Iso) was applied to decrease the dimensions of the original features and remove redundant information. Lastly, the marine predator algorithm-based support vector machine (MPA-SVM) classifier was employed to achieve intelligent fault diagnosis of planetary gearboxes. The suggested RCMFDE combines the composite coarse-grained construction and refined computing technology, overcoming unstable and invalid entropy in the traditional multiscale fluctuation dispersion entropy. Simulation experiments and fault diagnosis experiments from a real planetary gearbox drive system show that the complexity measure capability and feature extraction effectiveness of the proposed RCMFDE outperform the multiscale fluctuation dispersion entropy (MFDE) and multi-scale permutation entropy (MPE). The S-Iso’s visualization results and dimensionality reduction performance are better than principal components analysis (PCA), linear discriminant analysis (LDA), and isometric mapping (Isomap). Moreover, the suggested fault diagnosis scheme has an accuracy rate of 100% in identifying bearing and gear defects in planetary gearboxes. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Fault Diagnosis and Prognosis)

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25 pages, 5422 KiB

Open AccessArticle

Hybrid Feature Selection Framework for Bearing Fault Diagnosis Based on Wrapper-WPT

by Andrei S. Maliuk, Zahoor Ahmad and Jong-Myon Kim

Machines 2022, 10(12), 1204; https://doi.org/10.3390/machines10121204 - 12 Dec 2022

Cited by 6 | Viewed by 1527

Abstract

A framework aimed to improve the bearing-fault diagnosis accuracy using a hybrid feature-selection method based on Wrapper-WPT is proposed in this paper. In the first step, the envelope vibration signal of the roller bearing is provided to the Wrapper-WPT. There, it is initially [...] Read more.

A framework aimed to improve the bearing-fault diagnosis accuracy using a hybrid feature-selection method based on Wrapper-WPT is proposed in this paper. In the first step, the envelope vibration signal of the roller bearing is provided to the Wrapper-WPT. There, it is initially decomposed into several sub-bands using Wavelet Packet Transform (WPT), and a set out of nineteen time and frequency domain features are individually extracted from each sub-band of the decomposed vibration signal forming a wide feature pool. In the following step, Wrapper-WPT constructs a final feature vector using the Boruta algorithm, which selects the most discriminant features from the wide feature pool based on the important metric obtained from the Random Forest classifier. Finally, Subspace k-NN is used to identify the health conditions of the bearing, thus forming a hybrid signal processing and machine learning-based model for bearing fault diagnosis. In comparison with other state-of-the-art methods, the proposed method showed higher classification performance on two different bearing-benchmark vibration datasets with variable operating conditions. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Fault Diagnosis and Prognosis)

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22 pages, 7347 KiB

Open AccessArticle

Image-Processing-Based Intelligent Defect Diagnosis of Rolling Element Bearings Using Spectrogram Images

by Syed Muhammad Tayyab, Steven Chatterton and Paolo Pennacchi

Machines 2022, 10(10), 908; https://doi.org/10.3390/machines10100908 - 08 Oct 2022

Cited by 1 | Viewed by 1478

Abstract

Due to the excellent image recognition characteristics of convolutional neural networks (CNN), they have gained significant attention among researchers for image-processing-based defect diagnosis tasks. The use of deep CNN models for rolling element bearings’ (REBs’) defect diagnosis may be computationally expensive, and therefore [...] Read more.

Due to the excellent image recognition characteristics of convolutional neural networks (CNN), they have gained significant attention among researchers for image-processing-based defect diagnosis tasks. The use of deep CNN models for rolling element bearings’ (REBs’) defect diagnosis may be computationally expensive, and therefore may not be suitable for some applications where hardware and resources limitations exist. However, instead of using CNN models as end-to-end image classifiers, they can also be used to extract the deep features from images and those features can further be used as input to machine learning (ML) models for defect diagnosis tasks. In addition to extracting deep features using CNN models, there are also other methods for feature extraction from vibration characteristic images, such as the extraction of handcrafted features using the histogram of oriented gradients (HOG) and local binary pattern (LBP) descriptors. These features can also be used as input to classical ML models for image classification tasks. In this study, a performance comparison between all these image-processing-based defect diagnosis techniques was carried out in terms of fault detection accuracy and computational expense. Moreover, based upon the detailed comparison, a hybrid-ensemble method involving decision-level fusion is proposed, which is far less computationally expensive compared to CNN models while using them as end-to-end classifiers. The performance of all these models is also compared in the case of minimal training data availability and for diagnosis under slightly different operating conditions to ascertain their generalizability and ability to correctly diagnose despite the minimal availability of training data. The performance of the proposed hybrid-ensemble method remained outstanding for the REBs’ defect diagnosis despite the minimal of availability training data as well as the slight variation under operating conditions. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Fault Diagnosis and Prognosis)

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14 pages, 2598 KiB

Open AccessEditor’s ChoiceArticle

From Novelty Detection to a Genetic Algorithm Optimized Classification for the Diagnosis of a SCADA-Equipped Complex Machine

by Luca Viale, Alessandro Paolo Daga, Alessandro Fasana and Luigi Garibaldi

Machines 2022, 10(4), 270; https://doi.org/10.3390/machines10040270 - 09 Apr 2022

Cited by 7 | Viewed by 1793

Abstract

In the field of Diagnostics, the fundamental task of detecting damage is basically a binary classification problem, which is addressed in many cases via Novelty Detection (ND): an observation is classified as novel if it differs significantly from reference, healthy data. ND is [...] Read more.

In the field of Diagnostics, the fundamental task of detecting damage is basically a binary classification problem, which is addressed in many cases via Novelty Detection (ND): an observation is classified as novel if it differs significantly from reference, healthy data. ND is practically implemented summarizing a multivariate dataset with univariate distance information called Novelty Index. As many different approaches are possible to produce NIs, in this analysis, the possibility of implementing a simple classifier in a reduced-dimensionality space of NIs is studied. In addition to a simple decision-tree-like classification method, the process for obtaining the NIs can result as a dimension reduction method and, in turn, the NIs can be used for other classification algorithms. In addition, a case study will be analyzed thanks to the data published by the Prognostics and Health Management Europe (PHME) society, on the occasion of the Data Challenge 2021. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Fault Diagnosis and Prognosis)

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16 pages, 3395 KiB

Open AccessArticle

Multi-Sensor Data Driven with PARAFAC-IPSO-PNN for Identification of Mechanical Nonstationary Multi-Fault Mode

by Hanxin Chen, Yunwei Xiong, Shaoyi Li, Ziwei Song, Zhenyu Hu and Feiyang Liu

Machines 2022, 10(2), 155; https://doi.org/10.3390/machines10020155 - 18 Feb 2022

Cited by 35 | Viewed by 1829

Abstract

Data analysis has wide applications in eliminating the irrelevant and redundant components in signals to reveal the important informational characteristics that are required. Conventional methods for multi-dimensional data analysis via the decomposition of time and frequency information that ignore the information in signal [...] Read more.

Data analysis has wide applications in eliminating the irrelevant and redundant components in signals to reveal the important informational characteristics that are required. Conventional methods for multi-dimensional data analysis via the decomposition of time and frequency information that ignore the information in signal space include independent component analysis (ICA) and principal component analysis (PCA). We propose the processing of a signal according to the continuous wavelet transform and the construction of a three-dimensional matrix containing the time–frequency–space information of the signal. The dimensions of the three-dimensional matrix are reduced by parallel factor analysis, and the time characteristic matrix, frequency characteristic matrix, and spatial characteristic matrix are obtained with tensor decomposition. Through the comparative analysis of the simulation and the experiment, the time characteristic matrix and the frequency characteristic matrix can accurately characterize the normal and fault states of the mechanical equipment. On this basis, the authors established a probabilistic neural network classification model optimized by the improved particle swarm algorithm (IPSO). The parallel factor (PARAFAC) decomposition algorithm can extract features from the centrifugal pump experimental data for normal and multiple fault states, establish the mapping relationship of different fault features of the centrifugal pump in time, frequency, and space, and import the fault features into the model classification. The above measures can significantly improve the fault identification rate and accuracy for a centrifugal pump. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Fault Diagnosis and Prognosis)

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Review

Jump to: Research

36 pages, 16046 KiB

Open AccessEditor’s ChoiceReview

Vibration Image Representations for Fault Diagnosis of Rotating Machines: A Review

by Hosameldin Osman Abdallah Ahmed and Asoke Kumar Nandi

Machines 2022, 10(12), 1113; https://doi.org/10.3390/machines10121113 - 23 Nov 2022

Cited by 5 | Viewed by 3543

Abstract

Rotating machine vibration signals typically represent a large collection of responses from various sources in a machine, along with some background noise. This makes it challenging to precisely utilise the collected vibration signals for machine fault diagnosis. Much of the research in this [...] Read more.

Rotating machine vibration signals typically represent a large collection of responses from various sources in a machine, along with some background noise. This makes it challenging to precisely utilise the collected vibration signals for machine fault diagnosis. Much of the research in this area has focused on computing certain features of the original vibration signal in the time domain, frequency domain, and time–frequency domain, which can sufficiently describe the signal in essence. Yet, computing useful features from noisy fault signals, including measurement errors, needs expert prior knowledge and human labour. The past two decades have seen rapid developments in the application of feature-learning or representation-learning techniques that can automatically learn representations of time series vibration datasets to address this problem. These include supervised learning techniques with known data classes and unsupervised learning or clustering techniques with data classes or class boundaries that are not obtainable. More recent developments in the field of computer vision have led to a renewed interest in transforming the 1D time series vibration signal into a 2D image, which can often offer discriminative descriptions of vibration signals. Several forms of features can be learned from the vibration images, including shape, colour, texture, pixel intensity, etc. Given its high performance in fault diagnosis, the image representation of vibration signals is receiving growing attention from researchers. In this paper, we review the works associated with vibration image representation-based fault detection and diagnosis for rotating machines in order to chart the progress in this field. We present the first comprehensive survey of this topic by summarising and categorising existing vibration image representation techniques based on their characteristics and the processing domain of the vibration signal. In addition, we also analyse the application of these techniques in rotating machine fault detection and classification. Finally, we briefly outline future research directions based on the reviewed works. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Fault Diagnosis and Prognosis)

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