Classification Of Vibration Signals Research Articles

Research on fault diagnosis of rigid guide in hoist system based on vibration signal classification

Research on fault diagnosis of rigid guide in hoist system based on vibration signal classification

Application of Cluster Analysis for Classification of Vibration Signals from Drilling Stand Aggregates

Rotary drilling technology with diamond tools is still essential in progressively extracting the earth’s resources. Since investigating the disintegration mechanism in actual conditions is very difficult, the practice must start with laboratory research. Identifying and classifying the drilling stand and its aggregates as objects will contribute to the clarification of certain problems related to streamlining the process, optimizing the working regime, preventing emergencies, and reducing energy and economic demands. For these purposes, the cluster method was designed and applied. Applying the clustering method has a significant place in complex and dynamic processes. Eight vibration signals were measured and processed during the operation of the aggregates, such as the motor, pump, and hydrogenerator, with a sampling frequency of 18 kHz and a time interval of 30 s. Subsequently, 16 symptoms were designed and numerically calculated in the time and frequency domain, creating the symptom vector of the aggregate. The aim of the study and article was the classification of aggregates as objects into recognizable clusters. The results show that the strong symptoms include a measure of variability, variance in the signal, and kurtosis. The weak symptoms are skewness and the moment of the signal spectrum. Visualization in the symptom plane and space proved their influence on cluster formation. According to the cluster analysis results, six to seven clusters presenting the activity of the aggregates were classified. It was found that the boundaries between the clusters were not sharp. As part of the research, the centroids of clusters of aggregates and the distances between them were calculated. Classified clusters can rebuild reference clusters for objects with a similar character in a broader context.

Real-time classification for Φ-OTDR vibration events in the case of small sample size datasets

Efficient classification of vibration signals detected by phase-sensitive optical time domain reflectometer (Φ-OTDR) based on small samples is an effective method to reduce the false alarm rate without GPU or large data sets. This paper proposes a fiber optic system vibration event recognition method based on a combination of image segmentation pre-processing, texture, statistical, morphological feature extraction, and weighted support vector machine (WSVM), which can effectively classify-five types of vibration events in high-speed railway perimeter intrusion detection with small sample data and no parallel processing units. Erosion and dilation operations are applied to vibration signal image feature enhancement in image pre-processing. The vibration signal region and background are separated by the maximum inter-class variance method, then 35 features of the vibration signal region are calculated and finally employed to construct a WSVM. Experiments show that the method achieves 99 FPS and 98.8% accuracy on the test set with 330 vibration images as the training set to build the model without GPU and in the presence of interference signals. It provides a generalized Φ-OTDR vibration event recognition method for small samples.

A Study on Classification Method of Mine Vibration Based on Microseismic Monitoring Cloud Service Platform

With the gradual deepening of mining depth, sudden ground pressure disasters such as rock burst and collapse caused by deep high stress and high rock pressure are major hidden dangers affecting mine safety production. Microseismic monitoring technology has been widely applied in the field of mine ground pressure disaster warning. The existing microseismic monitoring system has some problems, such as inaccurate automatic recognition and classification of waveform signals, low quality and low efficiency of manual processing. In this paper, the automatic classification of vibration signals in the process of mining is studied, and the automatic classification results are uploaded to the cloud service platform in real time, which solves the technical bottleneck of the existing microseismic monitoring system. Meanwhile, the real-time monitoring of mine ground pressure safety is guaranteed based on the cloud service platform of microseismic monitoring.

Identification of Vibration Signal for Residual Pressure Utilization Hydraulic Unit Using MRFO-BP Neural Network

Combined with wavelet threshold denoising and Ensemble Empirical Mode Decomposition (EEMD) decomposition, an identification method based on Manta Ray Foraging Optimization-BP (MRFO-BP) neural network for vibration signals of residual pressure utilization hydraulic units is proposed to distinguish the vibration signal of each unit. The feature vectors of vibration signals are extracted by wavelet denoising and EEMD decomposition. The weights and thresholds in BP neural network are optimized by the MRFO algorithm. The feature vectors are input into the optimized BP neural network to realize the identification and classification of vibration signals. Compared with Particle Swarm Optimization-BP (PSO-BP) neural network, Bat Algorithm-BP (BA-BP) neural network, and BP neural network, the results show that the identification rate of each measuring point from the MRFO-BP neural network is greatly improved. The average identification rate of other measuring points is 98.514%, except the identification rate of the generator, which is 85.389%. Therefore, the MRFO-BP neural network has better stability and higher identification accuracy and can identify and classify vibration signals more accurately. The conclusions can provide theoretical basis for vibration signals identification of residual pressure utilization hydraulic unit. When the vibration signal of each unit cannot be clearly distinguished, the vibration signals of the units are identified by the method proposed in this paper. According to the obtained results, a feasible classification method can be provided for the vibration signals belonging to different units.

Vibration-based structural damage detection via phase-based motion estimation using convolutional neural networks

Detection of structural damage is a major concern for engineers. In recent years, convolutional neural networks (CNNs) have been used for feature extraction and classification of vibration signals that reveal structural damage. Damage detection by CNNs greatly depends on high-quality learning data which are usually difficult to be obtained in actual engineering scenarios. To solve this problem, we combine phase-based motion estimation (PME) with the use of CNNs. By PME method, each pixel in a video can be regarded as a separate displacement sensor. Thus, it is possible to obtain millions of vibration signals from a single video, greatly facilitating CNN applications. We used a two-story steel structure for experimental validation. It was demonstrated that only one measured video sample obtained under each structural condition is possible to train a CNN model accurately detecting the location and severity of bolt looseness damage. This verified the outstanding performance of the proposed method.

Analysis on transformer vibration signal recognition based on convolutional neural network

In order to study the relationship between the transformer vibration and the operation state, the wavelet analysis method and the convolutional neural network method were used to analyze the transformer vibration signal. This paper proposes a transformer based on convolution neural network-based surface vibration signal feature extraction method. The result show that the convolution of neural network in different station transformer surface vibration signal classification has a lot of advantage, as the integration of feature extraction and classification recognition process together can effectively classify vibration signal recognition processing. This method is feasible for classification and identification by providing an accuracy value of 92.74 %. The future perspective of this research will focus on a generalized network model and parameters through experimentation for further investigation of accuracy and efficiency of this method.

An end-to-end framework combining time–frequency expert knowledge and modified transformer networks for vibration signal classification

Vibration signal classification plays an important role in operation monitoring of mechanical structures. In this paper, a novel end-to-end framework is proposed for intelligent vibration signal classification including data preprocessing, time–frequency feature extraction, modified Transformer network as well as integral optimization. Two case studies in different engineering fields are conducted including the health monitoring of a bearing component through long-term running-to-failure experiments under constant loading conditions and the flight state identification of a novel self-sensing wing structure through a series of wind tunnel experiments under varying angles of attack and airspeeds. Multi-sensor fusion experiments are further conducted to enhance the classification accuracy. Results from both case studies demonstrate that the proposed method can not only extract distinguished high-level features but also be optimized jointly to achieve the best performance over convolutional neural network and recurrent neural network based methods in signal classification.

Knee joint vibration signal classification algorithm based on machine learning

The knee joint is the largest and most complex flexion and extension joint of the human body. It supports most of the weight of the human body during the whole body during standing or exercise. Because the knee joint has the characteristics of complex structure and large load, it is also vulnerable to damage. An effective diagnosis in the early stage of injury or lesion of the knee joint is of great help to the later treatment. At present, the commonly used knee joint examination methods have the problems of large trauma and high cost. Therefore, this paper uses machine learning technology to study the classification algorithm of knee joint vibration signal. The research results of this paper were verified by selecting the subjects to form a healthy group and a disease injury group. The experimental results show that the proposed signal denoising algorithm is superior to the traditional denoising algorithm. After analyzing several classification algorithms, the multi-classifier fusion algorithm has excellent performance in signal classification. The experimental results show that the research results can be applied to the classification of knee joint vibration signals, and then applied to the clinical diagnosis of knee joint diseases.

Autocorrelation Aided Random Forest Classifier-Based Bearing Fault Detection Framework

Rolling bearing defects in induction motors are usually diagnosed using vibration signal analysis. For accurate detection of rolling bearing defects, appropriate feature extraction from vibration signals is necessary, failure of which may lead to incorrect interpretation. Considering the above fact, this article presents an autocorrelation aided feature extraction method for diagnosis of rolling bearing defects. To this end, the vibration signals of healthy as well as different faulty bearings were recorded using accelerometers and autocorrelation of the respective vibration signals were done to examine their self-similarity in time scale. Following this, several statistical, hjorth as well as non-linear features were extracted from the respective vibration correlograms and were subjected to feature reduction using recursive feature elimination technique. The dimensionally reduced top ranked feature vectors were subsequently fed to a random forest classifier for classification of vibration signals. A large number of experiments were carried out for (i) three different fault diameters at (ii) four different shaft speeds and also at (iii) two different sampling frequencies. Besides, for each condition, six binary class and one multiclass classification problem is also addressed in this paper, resulting in a total 112 different classification tasks. It was observed that the proposed method has yielded very high accuracy in identifying different bearing defects which can be practically implemented for automated bearing fault detection of induction motors.

Fault State Recognition of Rolling Bearing Based Fully Convolutional Network

To solve the problem of determining the fault damage of rolling bearings, a fault diagnosis method for intelligent classification of vibration signals with different fault locations and different damage degrees is proposed. First, the research object is the laboratory dataset. By transforming into spectrograms, this can preserve the original information of the time-domain signal to a greater extent. Then, we use a deep, fully convolutional neural network to train the dataset. It has a rapid convergence and the accuracy is up to 100%. Second, in order to verify the correctness of the model, we take the service data on the real line as the research object, and the accuracy rate is as high as 99.22%. Compared with some other machine learning algorithms, our method boasts better generalization capability and accuracy and could be applied to practical engineering.

PCA Classification of vibration signals in WSN based oil pipeline monitoring system

Using wireless sensor network technology in structure health monitoring applications results in generating large amount of data. To sift through this data and extract useful information an extensive data analysis should be applied. In this paper, a Wireless Sensor Network (WSNs) is proposed for the oil pipeline monitoring system with proposed method for event detection and classification. The method depends on the Principal Component Analysis (PCA). It applied to features extracted from vibration signals of the monitored pipeline. These vibration signals are collected while applying damage events (knocking and drilling) to the oil pipeline. PCA is applied to features extracted from both time domain and frequency domain. The results manifest that this method is able to detect the existence of damage and also to distinguish between the different levels of harmful events applied to the pipeline.

Local and Global SR for Bearing Sensor-based Vibration Signal Classification

Local and Global SR for Bearing Sensor-based Vibration Signal Classification

Transform-domain sparse representation based classification for machinery vibration signals

The working state of machinery can be reflected by vibration signals. Accurate classification of these vibration signals is helpful for the machinery fault diagnosis. A novel classification method for vibration signals, named Transform Domain Sparse Representation-based Classification (TDSRC), is proposed. The method achieves high classification accuracy by three steps. Firstly, time-domain vibration signals, including training samples and test samples, are transformed to another domain, e.g. frequency-domain, wavelet-domain etc. Then, the transform coefficients of the training samples are combined as a dictionary and the transform coefficients of the test samples are sparsely coded on the dictionary. Finally, the class label of the test samples is identified by their minimal reconstruction errors. Although the proposed method is very similar to the Sparse Representation-based Classification (SRC), experimental results illustrates its performance is far superior to SRC in the classification of vibration signals. These experiments include: frequency-domain classification of bearing vibration data from the Case Western Reserve University (CWRU) Bearing Data Center and wavelet-domain classification of six fault-types gearbox vibration data from our rotating machinery experimental platform.

Low-Cost Vibration Sensor for Condition-Based Monitoring Manufactured From Polyurethane Foam

Ubiquitous vibration sensing forms a core requirement of Internet of Things (IoT) applications in condition-based monitoring (CbM). Such sensors can enable cost savings by identifying incipient failures in industrial machinery and, thereby, optimized maintenance schedule planning. Conventional piezoelectric and microelectromechanical systems (MEMS)-based vibration sensors developed for such applications cost upwards of tens and hundreds of dollars, limiting the scale of their deployment. In this article, we present an extremely inexpensive vibration sensor prepared with commercially available polyurethane foam that is commonly used for packaging of fragile goods. We present a process to coat the pores of the foam with conductive carbon ink to impart piezo-resistive properties to the material. A proof of concept realization of vibration sensor with 80-Hz sensing bandwidth is presented, along with experimental data demonstrating classification of vibration signals for different machine operating conditions. The spectral content of the measured vibration signal shows good agreement with spectral content of the audio recordings of corresponding acoustic measurements.

Classification of imbalance levels in a scaled wind turbine through detrended fluctuation analysis of vibration signals

This work proposes to identify different imbalance levels in a scaled wind turbine through vibration signals analysis. The experiment was designed in such a way that the acquired signals could be classified in different ways. A combination of detrended fluctuation analysis of acquired signals and different classifiers, supervised and unsupervised, was performed. The optimum number of groups suggested by k-means clustering, an automatic classifier with unsupervised learning algorithm, differs from the number of classes (or subsets) defined during the experimental planning, presenting another approach to the possible classification of vibration signals. Additionally, three supervised learning algorithms (namely neural networks, Gaussian classifier and Karhunen-Loève transform) were employed to this end, classifying the collected data in some predefined amounts of classes. The results obtained for the test data, just a little different regarding the training data, also confirmed their capability to identify new signals. The results presented are promising, giving important contributions to the development of an automatic system for imbalance diagnosis in wind turbines.

Classification of machinery vibration signals based on group sparse representation

The working condition of mechanical equipment can be reflected by vibration signals collected from it. Accurate classification of these vibration signals is helpful for the machinery fault diagnosis. In recent years, the L1-norm regularization based sparse representation for classification (SRC) has obtained huge success in image recognition, especially in face recognition. However, the investigation of SRC for machinery vibration signals shows that the accuracy and sparsity concentration index are not high enough. In this paper, a new classification method for machinery vibration signals is proposed, in which the L1L2-norm regularization based sparse representation, i.e. group sparse representation, is recommended as a coding strategy. The method achieves its idea classification performance by three steps. Firstly, time-domain vibration signals, including training and test samples, are transformed to frequency-domain to reduce the influence of corrupting noise. Then, the transform coefficient vectors of the test samples are coded with a combination of L1-norm and L2-norm constrain on a dictionary, which is constructed by merging the transform coefficient vectors of the training samples. At last, the fault types of the test samples are labeled by identifying their minimal reconstruction errors. The classification results of simulated and experimental vibration signals demonstrate the superiority of proposed method in comparison with the state-of-the-art classifiers.

Research of Mechanical Vibration Signal Classification Based on LMD

The traditional signal processing methods are difficult to accurately extract fault information, because mechanical fault vibration signals have non-stationary, which will cause system instability. Local mean decomposition is adaptive signal processing method. However, in the local mean decomposition of the signal, the trend of the endpoint can not be predicted which cause contaminating the entire signal sequence, the original moving average of the signal used over-smoothing treatment, resulting in fault characteristics can not accurately extract. The article introduces waveform matching to solve the original features of signals at the endpoints, using linear interpolation to get local mean and envelope function, then obtain production function PF vector through making use of the local mean decomposition. The energy entropy of PF vector take as identification input vectors. These vectors are respectively inputted BP neural networks, support vector machines, least squares support vector machines to identify faults. Experimental result show that the accuracy of least squares support vector machine with higher classification accuracy has been improved.

Vibration Monitoring Using Wavelets Transform Feature Extraction Algorithm and Technique

Vibration is a mechanical phenomenon whereby oscillations occur about an equilibrium point. The oscillation may be periods such as the motion of a pendulum or random such as the movement of tire on a gravel road. Vibration causes waste of energy and creates unwanted sound-noise. Monitoring such process generally possess a big problem especially for a system. The present traditional single resolution techniques could not solve this problem, coupled with the Fourier transform which seems to be one of the best method in analyzing and monitoring vibration in machineries or machinery components.This paper present a new algorithm using wavelet- packet based feature in classification of vibration signals. This study explores the feasibility of the wavelet packet transform as a tool in search for features that may be used in the detection and classification of machinery vibration signals. By formulating a systematic method of determining wavelet packet based features that exploit class specific differences among interested signals, which avoid human interaction. This new algorithm provide more effective method to achieve robust classification than traditional single resolution techniques. The new algorithm in wavelet transform techniques proved to be more efficient, better analysis, and provides better results with minimum error than any existing method.

Classification of time-frequency representations using improved morphological pattern spectrum for engine fault diagnosis

Time-frequency representations (TFR) have been intensively employed for analyzing vibration signals in the field of mechanical faults diagnosis. However, in many applications, TFR are simply utilized as a visual aid. It is very attractive to investigate the utility of TFR for automatic classification of vibration signals. A key step for this work is to extract discriminative parameters from TFR as input feature vector for classifiers. This paper contributes to this ongoing investigation by developing an improved morphological pattern spectrum (IMPS) for feature extraction from TFR. The S transform, which combines the separate strengths of the short time Fourier transform and wavelet transforms, is chosen to perform the time-frequency analysis of vibration signals. Then, we present an improved morphological pattern spectrum (IMPS) scheme, which utilizes the first moment replace of the volume measure used in traditional morphological pattern spectrum (MPS) method. The promise of IMPS is illustrated by performing our procedure on vibration signals measured from an engine with five operating states. Experimental results have demonstrated the presented IMPS to be an effective approach for characterizing the TFR of vibration signals in engine fault diagnosis.