A weak fault diagnosis method for rotating machinery based on compressed sensing and stochastic resonance

Vibration signals used for rotating machinery fault diagnosis often constitute large amount of data. It is a big challenge to extract faults feature information from these data. Recently, a new sampling framework called compressed sensing has been proposed, which enables the recovery from a small set of measured data if the signals are sparse or compressible. In reality, the sparseness of the signals is not very well due to noise, so it is difficult and unavailing to recover the whole signal. Thus, a new mechanical fault diagnosis method is proposed in this paper. First, the machine fault vibration signals are pretreated by stochastic resonance. By this way, the fault signal drowned by noise is amplified and the sparseness of the signals is enhanced, which make it possible to apply compressed sensing. Second, fault features are extracted directly from the compressed data without recovering completely, which reduces the dimensionality of the measurement data and the complexity of algorithm. Finally, the effectiveness of the proposed method is proved by the experiments

A new fault diagnosis method using deep belief network and compressive sensing

Compressive sensing provides a new idea for machinery monitoring, which greatly reduces the burden on data transmission. After that, the compressed signal will be used for fault diagnosis by feature extraction and fault classification. However, traditional fault diagnosis heavily depends on the prior knowledge and requires a signal reconstruction which will cost great time consumption. For this problem, a deep belief network (DBN) is used here for fault detection directly on compressed signal. This is the first time DBN is combined with the compressive sensing. The PCA analysis shows that DBN has successfully separated different features. The DBN method which is tested on compressed gearbox signal, achieves 92.5 % accuracy for 25 % compressed signal. We compare the DBN on both compressed and reconstructed signal, and find that the DBN using compressed signal not only achieves better accuracies, but also costs less time when compression ratio is less than 0.35. Moreover, the results have been compared with other classification methods

A new fault diagnosis method using deep belief network and compressive sensing

Compressive sensing provides a new idea for machinery monitoring, which greatly reduces the burden on data transmission. After that, the compressed signal will be used for fault diagnosis by feature extraction and fault classification. However, traditional fault diagnosis heavily depends on the prior knowledge and requires a signal reconstruction which will cost great time consumption. For this problem, a deep belief network (DBN) is used here for fault detection directly on compressed signal. This is the first time DBN is combined with the compressive sensing. The PCA analysis shows that DBN has successfully separated different features. The DBN method which is tested on compressed gearbox signal, achieves 92.5 % accuracy for 25 % compressed signal. We compare the DBN on both compressed and reconstructed signal, and find that the DBN using compressed signal not only achieves better accuracies, but also costs less time when compression ratio is less than 0.35. Moreover, the results have been compared with other classification methods

Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals

The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments

Compressive Sampling and Feature Ranking Framework for Bearing Fault Classification with Vibration Signals

Failures of rolling element bearings are amongst the main causes of machines breakdowns. To prevent such breakdowns, bearing health monitoring is performed by collecting data from rotating machines, extracting features from the collected data, and applying a classifier to classify faults. To avoid the burden of much storage requirements and processing time of a tremendously large amount of vibration data, the present paper proposes a combined Compressive Sampling (CS) based on Multiple Measurement Vector (MMV) and Feature Ranking (FR) framework to learn optimally fewer features from a large amount of vibration data from which bearing health conditions can be classified. The CS-based on MMV model is the first step in this framework and provides compressively-sampled signals based on compressed sampling rates. In the second step, the search for the most important features of these compressively-sampled signals is performed using feature ranking and selection techniques. For that purpose, we have investigated the following: (1) two compressible representations of vibration signals that can be used within CS framework, namely, Fast Fourier Transform (FFT) based coefficients and thresholded Wavelet Transform (WT) based coefficients, and (2) several feature ranking and selection techniques, namely, three similarity-based techniques, Fisher Score (FS), Laplacian Score (LS), Relief-F; one correlation-based technique, Pearson Correlation Coefficients (PCC); and one independence test technique, Chi-Square (Chi-2) to select fewer features that can sufficiently represent the original vibration signals. These selected features, in combination with three of the popular classifiers - multinomial Logistic Regression classifier (LRC), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs), have been evaluated for the classification of bearing faults. Results show that the proposed framework achieves high classification accuracies with a limited amount of data using various combinations of methods, which outperform recently published results

Intrinsic dimension estimation-based feature selection and multinomial logistic regression for classification of bearing faults using compressively sampled vibration signals

Acknowledgements: Authors wish to thank Brunel University London for their support. Data Availability Statement: The data presented in the first case study may be available on request from the first author, Hosameldin O. A. Ahmed.Copyright: © 2022 by the authors. As failures of rolling bearings lead to major failures in rotating machines, recent vibration-based rolling bearing fault diagnosis techniques are focused on obtaining useful fault features from the huge collection of raw data. However, too many features reduce the classification accuracy and increase the computation time. This paper proposes an effective feature selection technique based on intrinsic dimension estimation of compressively sampled vibration signals. First, compressive sampling (CS) is used to get compressed measurements from the collected raw vibration signals. Then, a global dimension estimator, the geodesic minimal spanning tree (GMST), is employed to compute the minimal number of features needed to represent efficiently the compressively sampled signals. Finally, a feature selection process, combining the stochastic proximity embedding (SPE) and the neighbourhood component analysis (NCA), is used to select fewer features for bearing fault diagnosis. With regression analysis-based predictive modelling technique and the multinomial logistic regression (MLR) classifier, the selected features are assessed in two case studies of rolling bearings vibration signals under different working loads. The experimental results demonstrate that the proposed method can successfully select fewer features, with which the MLR-based trained model achieves high classification accuracy and significantly reduced computation times compared to published research.This research received no external funding

A smart monitoring system for bearing fault detection

Rolling element bearings are commonly used in rotating machinery to support shafts, reduce friction, and increase power transmission efficiency. For a machinery system, bearing fault could be the most possible cause of mechanical failures. If bearing defect can be detected at its early stage, mechanical performance degradation and even economic losses can be avoided. Although many signal processing techniques have been proposed in the literature for bearing fault detection, reliable bearing fault diagnosis is still a challenging task in this R&D field, especially in industrial applications. The objective of this work is to develop a smart condition monitoring system and a signal processing technique for bearing fault detection. Firstly, a Field Programmable Gate Arrays (FPGA) based sinusoidal generator is developed to generate controllable sinusoidal waveforms and explore FPGA’s potential applications in a data acquisition system to collect vibration signals. Secondly, an adaptive variational mode decomposition (AVMD) technique is proposed for bearing fault detection. The AVMD includes several steps in processing: 1) Signal characteristics are analyzed to determine the signal center frequency and the related parameters. 2) The ensemble-kurtosis index is suggested to select the optimal intrinsic mode function (IMF) to decompose the target signal. 3) The envelope spectrum analysis is performed using the selected IMF to identify the representative features for bearing fault detection. The effectiveness of the proposed AVMD technique is examined by simulation and experimental tests under different bearing conditions, with the comparison of other related bearing fault techniques

Compressed Sensing for Open-ended Waveguide Non-Destructive Testing and Evaluation

Ph. D. ThesisNon-destructive testing and evaluation (NDT&E) systems using open-ended waveguide (OEW) suffer from critical challenges. In the sensing stage, data acquisition is time-consuming by raster scan, which is difficult for on-line detection. Sensing stage also disregards demand for the latter feature extraction process, leading to an excessive amount of data and processing overhead for feature extraction. In the feature extraction stage, efficient and robust defect region segmentation in the obtained image is challenging for a complex image background. Compressed sensing (CS) demonstrates impressive data compression ability in various applications using sparse models. How to develop CS models in OEW NDT&E that jointly consider sensing & processing for fast data acquisition, data compression, efficient and robust feature extraction is remaining challenges. This thesis develops integrated sensing-processing CS models to address the drawbacks in OEW NDT systems and carries out their case studies in low-energy impact damage detection for carbon fibre reinforced plastics (CFRP) materials. The major contributions are: (1) For the challenge of fast data acquisition, an online CS model is developed to offer faster data acquisition and reduce data amount without any hardware modification. The images obtained with OEW are usually smooth which can be sparsely represented with discrete cosine transform (DCT) basis. Based on this information, a customised 0/1 Bernoulli matrix for CS measurement is designed for downsampling. The full data is reconstructed with orthogonal matching pursuit algorithm using the downsampling data, DCT basis, and the customised 0/1 Bernoulli matrix. It is hard to determine the sampling pixel numbers for sparse reconstruction when lacking training data, to address this issue, an accumulated sampling and recovery process is developed in this CS model. The defect region can be extracted with the proposed histogram threshold edge detection (HTED) algorithm after each recovery, which forms an online process. A case study in impact damage detection on CFRP materials is carried out for validation. The results show that the data acquisition time is reduced by one order of magnitude while maintaining equivalent image quality and defect region as raster scan. (2) For the challenge of efficient data compression that considers the later feature extraction, a feature-supervised CS data acquisition method is proposed and evaluated. It reserves interested features while reducing the data amount. The frequencies which reveal the feature only occupy a small part of the frequency band, this method finds these sparse frequency range firstly to supervise the later sampling process. Subsequently, based on joint sparsity of neighbour frame and the extracted frequency band, an aligned spatial-spectrum sampling scheme is proposed. The scheme only samples interested frequency range for required features by using a customised 0/1 Bernoulli measurement matrix. The interested spectral-spatial data are reconstructed jointly, which has much faster speed than frame-by-frame methods. The proposed feature-supervised CS data acquisition is implemented and compared with raster scan and the traditional CS reconstruction in impact damage detection on CFRP materials. The results show that the data amount is reduced greatly without compromising feature quality, and the gain in reconstruction speed is improved linearly with the number of measurements. (3) Based on the above CS-based data acquisition methods, CS models are developed to directly detect defect from CS data rather than using the reconstructed full spatial data. This method is robust to texture background and more time-efficient that HTED algorithm. Firstly, based on the histogram is invariant to down-sampling using the customised 0/1 Bernoulli measurement matrix, a qualitative method which only gives binary judgement of defect is developed. High probability of detection and accuracy is achieved compared to other methods. Secondly, a new greedy algorithm of sparse orthogonal matching pursuit (spOMP)-based defect region segmentation method is developed to quantitatively extract the defect region, because the conventional sparse reconstruction algorithms cannot properly use the sparse character of correlation between the measurement matrix and CS data. The proposed algorithms are faster and more robust to interference than other algorithms.China Scholarship Counci

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries