Health Monitoring Of Rotating Machines Research Articles

A Wasserstein generative digital twin model in health monitoring of rotating machines

Artificial intelligence-based rotating machine health monitoring and diagnosis methods often encounter problems, such as a lack of faulty samples. Although the simulation-based digital twin model may potentially alleviate these problems with sufficient prior knowledge and a large amount of time, the more demanding requirements of adaptivity, autonomy, and context-awareness may not be satisfied. This study attempted to address these problems by proposing a novel digital twin model referred to as the Wasserstein generative digital twin model (WGDT). The model employs a Wasserstein generative adversarial network (WGAN) as its core to model virtual samples with high fidelity to the healthy physical samples obtained from different industrial assets, thereby meeting the adaptivity requirement. Further, through a designed consistency test criterion mechanism, samples with high fidelity were generated by checking the similarity of distributions between generated samples and healthy physical samples to ensure that training process in conducted in a timely manner and manual involvement is avoided, thereby catering to the need for autonomy. This mechanism is based on the synchronous evolution of the generator and critic during training. Furthermore, the structure of the critic network can be customized according to the service-end tasks and testing conditions, thereby fulfilling the context awareness requirement. Subsequently, the critic network in the Wasserstein generative adversarial network (WGAN) can be used to perform different service-end tasks. The performance of the digital twin model was evaluated using two experimental cases and the results indicated that the WGDT model can efficiently and stably perform service-end tasks such as health monitoring, early fault detection, and degradation tracking without the requirement of prior knowledge, historical test samples, and faulty samples regarding the asset.

Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis

Rotating machine health monitoring is critical for system safety, cost savings, and increased reliability. The need for a simple and accurate fault diagnosis method has led to the development of various monitoring techniques. They incorporate vibration, motor’s current signature, and acoustic emission signals analysis in condition monitoring. So, based on using vibration signal analysis, a test rig was built for bearing fault identification. The test rig replicates and investigates various bearing problems, such as those found in the inner and outer races. An accelerometer, type ADXL335, was interfaced to a data acquisition device (DAQ USB-6215) for collecting vibration signals under various operating circumstances. In addition, a load cell was embedded with the test rig, interfaced with a digital panel meter, and used for recording the applied load on the bearings. The time-domain signal analysis technique was used after acquiring vibration signals at various bearing health states. Then, the time-domain signal was converted to the frequency domain using the fast Fourier transform, and the result was analyzed to investigate the generated fault frequencies. Finally, the obtained frequencies were compared with the theoretical values extracted from the theoretical equations, and the method proved its effectiveness in detecting the fault generated.

Fault Feature Recovery With Wasserstein Generative Adversarial Imputation Network With Gradient Penalty for Rotating Machine Health Monitoring Under Signal Loss Condition

Rotating machine health monitoring systems sometimes suffer from large segments of continuous missing data in practical applications, which may lead to incorrect health assessments and maintenance decisions. However, current signal loss recovery methods are sometimes unable to impute loss monitoring signals and directly recover fault features without an intact training dataset. Moreover, these methods often fail to accurately capture the data distribution and recover the fault features when processing monitoring signals with large segments of continuous missing data and relatively high noise pollution from the rotating machinery. In this study, a fault feature recovery strategy called the Wasserstein generative adversarial imputation network with gradient penalty (WGAIN-GP) is proposed. The generative adversarial training strategy can help capture the distribution of the original monitoring signal. The mask mechanism ensures that the fault feature recovery method can be performed in an unsupervised manner and directly imputes the original loss signal and recovers the fault features. Finally, the introduction of the Wasserstein distance loss function and the gradient penalty further ensures that the fault features can be restored with relatively high accuracy and a lower probability of mode collapse. Case studies were conducted using both simulations and rotating machine experiments to demonstrate the effectiveness of the proposed method.

Denoising convolutional autoencoder configuration for condition monitoring of rotating machines

In the present state of technology, rotating machine health monitoring is becoming economically mandatory. There is presently a clear trend to apply deep learning techniques to this area. This work analyzes some of the main supervised and unsupervised neural network current architectures and, as a consequence, proposes a novel denoising convolutional autoencoder model configuration applied to machine health monitoring. The configuration aims the main advantages of different conventional architectures for performance optimization over normalized time series signals. Experiments carried out based on vibration data extracted from bearings helped to validate the developed model and to determine the structure that offer the most satisfactory results. A comparison is made in terms of accuracy and robustness to noise between the proposed method and some related methods, including other published models. The objective is to assess the effectiveness of the proposed method through using two public experimental datasets produced under different machine operating conditions. The experimental results show that the proposed architecture provides greater classification accuracy and robustness in both case studies considering other published models.

Theoretical and Experimental Investigations on Spectral Lp/Lq Norm Ratio and Spectral Gini Index for Rotating Machine Health Monitoring

Prognostics and health management of the rotating machine aim to use monitoring data to infer the health conditions of the rotating machine in order to avoid unexpected accidents and minimize economic losses. Since health indices can detect an abnormality and provide observations for prognostic modeling, they are the basis of prognostics and health management. The spectral <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lp</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lq</i> norm ratio and the spectral Gini index have been recognized as popular health indices to characterize the impulsiveness of repetitive transients caused by machine faults for rotating machine health monitoring. Here, some special forms of the spectral <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lp</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lq</i> norm ratio include spectral kurtosis, the spectral <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L2</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L1</i> norm ratio, the reciprocal of the spectral smoothness index, and so on. In this article, theoretical and experimental investigations on the spectral <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lp</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lq</i> norm ratio and the spectral Gini index for machine health monitoring are conducted to prove how they characterize the impulsiveness of repetitive transients. Results showed that an increase in the total length of the nonimpulsive regions of repetitive transients makes the spectral <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lp/Lq</i> norm ratio and the spectral Gini index become large, which, in turn, can be used to explain changes of health indices during machine degradation at varying operating conditions and in the case of impulsive noises. To solve the problem of the sensitiveness of popular health indices to impulsive noises, a fused health index for characterizing cyclostationarity of repetitive transients is proposed. Analyses of bearing run-to-failure showed that the proposed fused index has better monitoring performance than the aforementioned popular health indices. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the problem of characterizing nonprocessed signals for automatic machine health monitoring. Practically, nonprocessed signals, such as vibration signals, acoustic signals, and so on, cannot directly be applied to reflect machine health conditions. The transformation of nonprocessed signals by using health indices into process signals is great of a concern. Most existing methods directly use intuition and experience to choose health indices in order to realize machine health monitoring. Thus, these methods do not provide theoretical investigations and support to illustrate how health indices characterize nonprocessed signals generated from a faulty machine. This article uses mathematical models and inferences to explain how popular health indices characterize impulsive signals generated from a faulty machine. Then, it is shown that direct applications of popular health indices in a time domain are sensitive to impulsive noises, which causes failures of health indices for machine health monitoring in the occurrence of impulsive noises. To solve this problem, it is suggested to use indices to characterize frequency information of faulty signals. Finally, an efficient and reliable fusion of health indices in a frequency domain is proposed for machine health monitoring.

Stationary Wavelet-Fourier Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis.

Bearing fault diagnosis methods play an important role in rotating machine health monitoring. In recent years, various intelligent fault diagnosis methods have been proposed, which are mainly based on the features extraction method combined with either shallow or deep learning methods. During the last few years, Shannon entropy features have been widely used in machine health monitoring, improving the accuracy of the bearing fault diagnosis process. Therefore, in this paper, we consider the combination of multi-scale stationary wavelet packet analysis with the Fourier amplitude spectrum to obtain a new discriminative Shannon entropy feature that we call stationary wavelet packet Fourier entropy (SWPFE). Features extracted by our SWPFE method are then passed onto a shallow kernel extreme learning machine (KELM) classifier to diagnose bearing failure types with different severities. The proposed method was applied on two experimental vibration signal databases of a rolling element bearing and compared to two recently proposed methods called stationary wavelet packet permutation entropy (SWPPE) and stationary wavelet packet dispersion entropy (SWPPE). Based on our results, we can say that the proposed method is able to achieve better accuracy levels than both the SWPPE and SWPDE methods using fewer failure features. Further, as our method does not require any hyperparameter calibration step, it is less dependent on user experience/expertise.

A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis

Abstract Bearing fault diagnosis is a significant part of rotating machine health monitoring. In the era of big data, various data-driven methods, which are mainly based on deep learning (DL), have been applied in the field of fault diagnosis. As a common deep learning method, convolutional neural networks play an important role in the field of image classification. To take advantage of existing research results of convolutional neural networks that were originally designed for images, short-time Fourier transform is applied to convert signals to two-dimensional graphs. However, the traditional convolutional neural network with max-pooling layers may disregard the positional relationship between features. Inspired by dynamic routing capsule net, a novel capsule network with an inception block and a regression branch is proposed. First, during the preprocessing stage, the one-dimensional signals are transformed into a time-frequency graph. Second, the graph data are fed into the network, and two convolution layers are applied to extract higher information; following that, an inception block is applied to the output feature maps to improve the nonlinearity of the capsule. Then, after dynamic routing, the lengths of the capsules are used to classify the fault category. There are two other branches: one uses the longest capsule to regress the damage size of the capsule, and the other reconstructs the input graph. To verify the generalization ability of the proposed model, comparisons among the proposed model, conventional methods and state-of-the-art convolutional neural network models are carried out.

Diversity and Integration of Rotating Machine Health Monitoring Methods

Health monitoring for rotating machines is investigated through two kinds of mock-up experimental data analysis. One is an anomaly mock-up test of roll bearing type rotating machine. Here, inner ring defect anomaly is simulated and its operating data are measured by both attached type accelerometer sensor and non-attached type microphone. Three kinds of signal pre-processing methods, frequency spectrum, principal component analysis and cepstrum, are applied to discriminate normal and abnormal states using several different classification algorithms, such as adaboost or random forest. Through analysis of their performance with the help of receiver operating characteristic (ROC) curve, the importance of diversified health monitoring methods is discussed. Another mock-up experiment is an accelerated test of roll bearing wear. Here, acoustic emission counts, accelerometer signal and wear particle number in lubricating oil are measured. Using these observation data, we make clear the relationships between deterioration mechanisms of bearing and behaviour of different observations.