Wavelet Packet Energy Features Research Articles

Intelligent Diagnosis of Compound Faults of Gearboxes Based on Periodical Group Sparse Model

A gearbox compound fault intelligent diagnosis method based on the period group sparse model is proposed for the problem that the fault features are coupled with each other and the fault components are superimposed on each other and difficult to be separated in the gearbox compound fault signal. Firstly, a binary sequence is constructed to embed the fault pulse period as a priori knowledge into the group sparse model to decouple and separate the composite fault signal while maintaining the amplitude and sparsity of the extracted features. Secondly, the wavelet packet energy features of the decoupled signals are extracted to improve the data quality while enhancing the characterization ability of the dictionary in the classification model. Finally, the wavelet packet energy features are imported into the sparse dictionary classification model, and the fault diagnosis is completed by outputting the fault categories using the self-driven characteristics of the data. The results show that the fault identification accuracy using the proposed method is 97%. In addition, the experimental validation under different states and working conditions with different rotational speeds allows the superiority and effectiveness of the algorithm in this paper to be tested and has the feasibility of a practical application in engineering.

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

In the context of addressing the challenge posed by limited fault samples in agricultural machinery rolling bearings, especially when early fault characteristics are subtle, this study introduces a novel approach. The proposed multi-domain fault diagnosis method, anchored in data augmentation, aims to discern early faults in agricultural machinery rolling bearings, particularly within an imbalanced sample framework. The methodology involves determining early fault signals throughout the life cycle, constructing early fault datasets with varying imbalance rates for different fault types, and subsequently employing the Synthetic Minority Oversampling Technique (SMOTE) to balance the fault data. The study then extracts relative wavelet packet energy and time-domain sensitive features (variance, peak to peak) from the original and generated fault data to form a multi-domain fault feature vector. This vector is utilized for fault state recognition using a Support Vector Machine (SVM). Evaluation metrics such as accuracy, recall, and F1 values assess the recognition effectiveness for each rolling bearing state, with the overall model recognition evaluated based on accuracy. The proposed method is rigorously analyzed and validated using the XJTU-SY rolling bearing accelerated life test dataset. Comparative analysis is conducted with non-data enhanced fault feature vectors, specifically the relative energy of the wavelet packet, both with and without time-domain features. Experimental results underscore the superior performance of multi-domain fault features in providing a comprehensive description of signal information, leading to enhanced classification performance. Furthermore, the study demonstrates improved classification accuracy and recall rates for the balanced dataset compared to the imbalanced dataset. This research significantly contributes to an effective identification method for the early fault diagnosis of small sample rolling bearings in agricultural machinery.

Series Arc Fault Detection Based on Multimodal Feature Fusion.

In low-voltage distribution systems, the load types are complex, so traditional detection methods cannot effectively identify series arc faults. To address this problem, this paper proposes an arc fault detection method based on multimodal feature fusion. Firstly, the different mode features of the current signal are extracted by mathematical statistics, Fourier transform, wavelet packet transform, and continuous wavelet transform. The different modal features include one-dimensional features, such as time-domain features, frequency-domain features, and wavelet packet energy features, and two-dimensional features of time-spectrum images. Secondly, the extracted features are preprocessed and prioritized for importance based on different machine learning algorithms to improve the feature data quality. The features of higher importance are input into an arc fault detection model. Finally, an arc fault detection model is constructed based on a one-dimensional convolutional network and a deep residual shrinkage network to achieve high accuracy. The proposed detection method has higher detection accuracy and better performance compared with the arc fault detection method based on single-mode features.

Detection of pulmonary arterial hypertension associated with congenital heart disease based on time–frequency domain and deep learning features

The heart sounds reflect the health of the heart. Its recording is the phonocardiogram (PCG). Pulmonary arterial hypertension associated with congenital heart disease (CHD-PAH) is a serious heart disease and is often associated with severe disability and death. The disease is not well characterized onset. The most patients are severe when they have been diagnosed and miss the best time to treat them. The objective of this study was to develop a computer aided diagnosis, which based on single cycle with multiple features, for detecting pulmonary arterial hypertension associated with congenital heart disease. It is a non-invasive and simple method which may be hopeful at early diagnosis of CHD-PAH. The original heart sounds were pre- processed first, in which a double-threshold adaptive segmentation method was used to segment the signal into each cardiac cycle first. Then the time–frequency domain features and wavelet packet energy features of cardiac cycle and S2 component are extracted. And convolutional neural network (CNN) is used to extract the depth features of cardiac cycle. The above features were combined into a fused feature vector. Normal, CHD and CHD-PAH were classified using XGBoost as the classifier. Finally, the majority voting algorithm is used to obtain the best classification result for multiple results corresponding to multiple cardiac cycles of the same person. Using this new method, a classification accuracy of 88.61% was achieved.

Detection of pulmonary hypertension associated with congenital heart disease based on time-frequency domain and deep learning features

The heart sounds reflect the health of the heart. Its recording is the phonocardiogram (PCG). Pulmonary hypertension associated with congenital heart disease (CHD-PAH) is a serious heart disease and is often associated with severe disability and death. The disease is not well characterized onset. The most patients are severe when they have been diagnosed and miss the best time to treat them. The objective of this study was to develop a computer aided diagnosis, which based on single cycle with multiple features, for detecting pulmonary hypertension associated with congenital heart disease. It is a non-invasive and simple method which may be hopeful at early diagnosis of CHD-PAH. The original heart sounds were pre-processed first, in which a double-threshold adaptive segmentation method was used to segment the signal into each cardiac cycle first. Then the time–frequency domain features and wavelet packet energy features of cardiac cycle and S2 component are extracted. And convolutional neural network (CNN) is used to extract the depth features of cardiac cycle. The above features were combined into a fused feature vector. Normal, CHD and CHD-PAH were classified using XGBoost as the classifier. Finally, the majority voting algorithm is used to obtain the best classification result for multiple results corresponding to multiple cardiac cycles of the same person. Using this new method, a classification accuracy of 88.61% was achieved.

Fault Diagnosis of Check Valve Based on KPLS Optimal Feature Selection and Kernel Extreme Learning Machine

The check valve is the core part of high-pressure diaphragm pumps. It has complex operation conditions and has difficulty characterizing fault states completely with its single feature. Therefore, a fault signal diagnosis model based on the kernel extreme learning machine (KELM) was constructed to diagnose the check valve. The model adopts a multi-feature extraction method and reduces dimensionality through kernel partial least squares (KPLS). Firstly, we divided the check valve vibration signal into several non-overlapping samples. Then, we extracted 16 time-domain features, 13 frequency-domain features, 16 wavelet packet energy features, and energy entropy features from each sample to construct a multi-feature set characterizing the operation state of the check valve. Next, we used the KPLS method to optimize the 45 dimension multi-feature data and employed the processed feature set to establish a KELM fault diagnosis model. Experiments showed that the method based on KPLS optimal feature selection could fully characterize the operating state of the equipment with an accuracy rate of 96.88%. This result indicates the high accuracy and effectiveness of the multi-feature set constructed with the KELM fault diagnosis model.

A relaxed support vector data description algorithm based fault detection in distribution systems

The power detection of the distribution network is essential for reliable and secure distribution. In this paper, a flexible dual-threshold SVDD fault warning algorithm with fault samples is proposed to deal with problems concerning complex network topology, accessible data, and missing fault data in the power grid. For the problems of complicated network topology and a wide variety of signal types, we propose to combine wavelet packet energy features with Spearman to extract electric signal features, and finally achieve accurate feature extraction of multiple signal types. In the case of the problems of untimely judgement and low accuracy of the original SVDD, a relaxed SVDD fault warning algorithm with fault samples is correspondingly proposed. We turn the original SVDD boundary into a double-layer boundary, and divides the hypersphere space into three regions to increase the sensitivity to the fault samples and lessen the risk of missed detection. Besides, an adaptive update strategy is developed, which reduces the computational effort of the model and is proven more applicable to the distribution. Finally, the method is applied to numerical examples and fault detection experiments, and the experimental results in turn verified its effectiveness and superiority.

Anomaly Data Detection of Rolling Element Bearings Vibration Signal Based on Parameter Optimization Isolation Forest

Anomaly data detection is not only an important part of the condition monitoring process of rolling element bearings, but also the premise of data cleaning, compensation and mining. Aiming at the abnormal data segment detection of the vibration signals of a rolling element bearing, this paper proposes an abnormal data detection model based on comprehensive features and parameter optimization isolation forest (CF-POIF), which can adaptively identify abnormal data segments. First, in order to extract the mutation feature of vibration signals more accurately, the concept of comprehensive feature is proposed, which integrates the time domain and wavelet packet energy features. Then, the particle swarm optimization (PSO) algorithm is used to optimize the rectangular window length and sub sample set capacity in the isolation forest for anomaly detection. Finally, three real cases concerning abnormal data are used to verify the effectiveness of the proposed method. The results demonstrate that the proposed method is able to detect missing data, drift data and external interference data effectively, and it has a higher F1 score and accuracy compared to other methods.

Computerized lung sound based classification of asthma and chronic obstructive pulmonary disease (COPD)

Diagnostic ambiguity between chronic pulmonary diseases like asthma and Chronic Obstructive Pulmonary Disease (COPD) is very high, as they exhibit similar symptoms, which is the factor responsible for misdiagnosis, leading to heavy deaths every year. To prevent misdiagnosis, some useful work is highly required. This article presents the implementation of a computerized lung sound (LS) based method to classify asthma and COPD cases.The study is conducted on 80 asthma, 80 COPD and 80 healthy LSs.The LS denoising is carried out using empirical mode decomposition (EMD), Hurst analysis and spectral subtraction method. Wavelet entropy (WE) and wavelet packet energy (WPE) features of LS’s are extracted. Various classifiers like support vector machine (SVM), decision tree (DT), k-nearest neighbor (KNN) and discriminant analysis (DA) are accessed to classify healthy, COPD and asthma using WE and WPE features of LS to produce better outcomes. Using the proposed algorithm, the study discriminates between healthy, asthma and COPD cases based on LS with a considerable classification accuracy of 99.3% using the decision tree (DT) classifier. Thus, the study confirms the successful differentiation of asthma and COPD based on LS. Future endeavours will be based on the validation of this algorithm to distinguish the real-time LS data acquired from asthmatic and COPD patients.

Intelligent Fault Diagnosis of Machines Based on Adaptive Transfer Density Peaks Search Clustering

Intelligent fault diagnosis technology of the rotating machinery is an important way to guarantee the safety of industrial production. To enhance the accuracy of autonomous diagnosis using unlabelled mechanical faults data, a novel intelligent diagnosis algorithm has been developed for rotating machinery based on adaptive transfer density peak search clustering. Combined with the wavelet packet energy feature extraction algorithm, the proposed algorithm can enhance the computational accuracy and reduce the computational time consumption. The proposed adaptive transfer density peak search clustering algorithm can adaptively adjust the classification parameters and mark the categories of unlabelled experimental data. Results of bearing experimental analysis demonstrated that the proposed technique is suitable for machinery fault diagnosis using unlabelled data, compared with other traditional algorithms.

Study on fault diagnosis method of splitter intelligent wheelchair motor based on wavelet and energy feature

As a key component of a split-type intelligent sports wheelchair for the disabled, the reliability of the motor is related to the personal safety of the wheelchair user and the accurate realization of the wheelchair’s sports functions. This motor is actually just a rotating machine. In order to achieve detection and analysis of rotating machinery bearing vibration signal, a method based on wavelet and energy feature of rotating machinery fault diagnosis is introduced. This method applies wavelet to obtain de-noising and then uses wavelet packet energy feature extraction to diagnose faults effectively caused by rotating machinery such as rotor unbalance fault, rotor misalignment fault, and rotor-to-stator rub fault. Test results illustrate that when different faults occur to the bearing, it is viable to utilize pattern recognition to diagnose faults for the reason that discrepancies appear in sub-hand energy after wavelet packet decomposition. The main research conclusions of this paper are also directly applied to the fault diagnosis of such wheelchair motors.

Weld Seam Deviation Prediction of Gas Metal Arc Welding Based on Arc Sound Signal

Weld seam deviation prediction is the key to weld seam tracking control, which is of great significance for realizing welding automation and ensuring welding quality. Aiming at the problem of weld seam deviation prediction in GMAW (gas metal arc welding), a method of weld seam deviation prediction based on arc sound signal is proposed. By analyzing the feature of the arc sound signal waveform, the time domain feature of the arc sound signal is extracted. The wavelet packet analysis method is used to analyze the time-fre- quency domain feature of the arc sound signal, and the wavelet packet energy feature is extracted. The time domain feature and wavelet packet energy feature are used to establish the feature vector, and the BP (back propagation) neural network is used to realize the weld seam deviation prediction. The results show that the method proposed in this paper has a good weld seam deviation prediction effect, with a mean absolute error of 0.234 mm, which provides a new method for GMAW weld seam recognition.

A preliminary study of classification of upper limb motions and forces based on mechanomyography

Rehabilitation training is essential for patients who have a history of certain illnesses, such as stroke. As a crucial part of rehabilitation training, upper limb training involves such key factors as upper limb motions and forces. This study investigated three upper limb motions (elbow flexion of 135°, Motion 1; shoulder flexion of 90°, Motion 2; and shoulder abduction of 90°, Motion 3) and various forces (muscle Force 0, no force; holding one 1.4 kg dumbbell, muscle Force 1; holding one 2.4 kg dumbbell, muscle Force 2) in combination to evaluate nine motion patterns. These patterns were completed by twelve healthy volunteers. Mechanomyography (MMG) measurements of the biceps brachii (Channel 1), triceps (Channel 2), and deltoid (Channel 3) muscles were collected. These were subsequently divided into signal segments corresponding to each of the motions using a segmentation method based on average energy. After extracting time-domain features and wavelet packet energy features, support vector machine analysis (SVM) was used for the classification of the upper limb motions and forces based on the MMG measurements. Channel 2 and Channel 3 were shown to play an important role in the classification of upper limb motions, and Channel 1 played a role in the classification of the forces. These results demonstrate that collection of MMG measurements from the three muscles is feasible and suggest a foundation for further studies in which rehabilitation training is evaluated based on MMG measurements.

A decision support system for automatic sleep staging from HRV using wavelet packet decomposition and energy features

Heart rate variability (HRV) can reflect the relationship between heart rhythm and sleep structure. In order to achieve long-term effective monitoring of sleep and to solve the generalization problem of sleep staging algorithms, HRV signals were used to identify the wake, non-rapid eye movement (NREM) and rapid eye movement (REM) stages of two databases with 11,597 epoches of 16 subjects (8 healthy subjects and 8 sleep disorder). Features were extracted from HRV using three different methods: Hilbert Huang Transform (HHT), Singular Value Decomposition (SVD), and Wavelet Packet Decomposition (WPD). The extracted features were reconstructed into a new sleep score table, classified using random forest (RF) classifier and the results were interpreted using basic statistical criteria. Among these methods, the WPD extraction feature is the most successful. For the sleep staging model trained in the first database, the accuracy of Wake, NREM and REM after WPD extraction features were 80.9%, 88.2% and 65.8% respectively. The second databases were tested using a trained model, in which the Avg.Acc, Avg.Precision, Avg.F and Kappa statistic for healthy subjects were 72.9%, 0.720, 0.716 and 0.439 ± 0.0346. The standard explanations for sleep disorder subjects were 77.8%, 0.780, 0.843 and 0.656 ± 0.0324. The experimental results show that the energy characteristics of WPD extraction are the best. In addition, there was a significant difference in Kappa values (p < 0.05) between healthy subjects and sleep disorder subjects by variance analysis of standard interpretation, and it can be used as a basis for judging whether a subject has a sleep disorder.

Automatic detection of glottal stop in cleft palate speech

The speech therapy is essential during the whole treatment of Cleft Palate (CP) over many years. The automatic evaluation of CP speech could provide an effective assistant diagnosis. The glottal stop is a typical compensatory articulation error in CP speech. It is produced by adducting the vocal folds as a substitution during the pronunciation of oral pressure consonants. The existence of glottal stop has a great impact on speech intelligibility. In this work, an automatic glottal stop detection system is proposed. The CP speech database is collected by the Hospital of Stomatology, Sichuan University, which has the largest number of CP patients in China. This database includes extensive CP speech samples annotated by speech-language pathologists at the phoneme level. The vocabulary of this database includes all the initial consonants in Mandarin. The automatic initials and finals segmentation method is proposed firstly. Then, for the pressure initial consonants, four types of acoustic features are extracted: Mel-frequency cepstral coefficients, formants, Gammatone filtering energy feature, wavelet packet energy and Shannon entropy features. The features are processed using a k-nearest neighbor classifier. The k-fold cross validation is used to calculate the average detection accuracy, which reaches up to 93.5% using Gammatone filtering energy feature.

Fault Diagnosis System Research of Large-scale Linear Vibrating Screen

The paper takes side panel cracks of large-scale Linear vibrating screen as research object and designs the hardware and upper computer diagnosis interface of fault diagnosis system. The side panel vibration signals have been de-noised and the wavelet packet energy feature has been extracted in Matlab with wavelet analysis theory. Meanwhile, the fault characteristic is sent to genetic neural network for identification. The practical manufacturing application shows that this kind of side panel crack fault diagnosis system of large-scale vibrating screen based on wavelet analysis has high fault recognition ability, classification precision and speed.

Vibration based diagnosis of wheel defects of metro train sets using one period analysis on the wayside

This research examines two different methods; Wavelet Packet Energy (WPE) and Time-domain Features (TDF) which are effective in faulty signal feature extraction of metro wheels in wayside level using vibration sensors. Signals of each wheelset passing of a trainset with both healthy and faulty wheels are recorded by the vibration sensors which are mounted on both left and right rails and a novel one-period sampling is performed at 51.2 kHz sample rate. Retrieved signal samples are used in the construction of a database which is consistent of healthy and faulty cases. Since the database has insufficient number of faulty samples, the database is balanced by a method so called Adaptive Synthetic Sampling (ADASYN) so that each class has the same number of observations. Two state-of art classifiers; Support Vector Machines (SVM) and Fisher Linear Discriminant Analysis (FLDA) are employed by utilizing 16-fold cross validation to solve the two-class problem. Referring to the results, SVM-I-TDF outperforms by classifying all samples with a success rate of 100 % and other methods have also promising results. Proposed methods may be used in the condition monitoring of metro wheelsets effectively by means of not only performance but also cost-efficiency.

Microseismic Signal Spectra, Energy Characteristics, and Fractal Features Prior to Rock Burst: A Case Study from the Qianqiu Coal Mine, China

Microseismic (MS) technology has been widely adopted for monitoring coal and rock dynamic disasters. Insights into MS wave characteristics contribute to the accurate prediction of these disasters. In this study, MS wave characteristics were analysed from three aspects: the signal spectra, wavelet packet energy and fractal features. It is shown that prior to the rock burst, the MS wave main frequency decreased following a power law, the amplitude linearly increased, the wavelet packet energy tended to become concentrated on the low frequency bands, and the correlation dimension decreased. When the rock burst occurred, the MS wave main frequency, wavelet packet energy and correlation dimension declined to their lowest levels. Meanwhile, the amplitude rose to a maximum. Therefore, the MS wave characteristics in this study were found to effectively identify and extract precursor information of value for predicting rock dynamic disasters.

Nondestructive diagnosis of flip chips based on vibration analysis using PCA-RBF

Flip chip technology combined with solder bump interconnection has been widely applied in IC package. The solder bumps are sandwiched between dies and substrates, leading to conventional techniques being difficult to diagnose the flip chips. Meanwhile, these conventional diagnosis methods are usually performed by human visual judgment. The human eye-fatigue can easily cause fault detection. Thus, it is difficult and crucial to detect the defects of flip chips automatically. In this paper, a nondestructive diagnosis system based on vibration analysis is proposed. The flip chip is excited by air-coupled ultrasounds and raw vibration signals are measured by a laser scanning vibrometer. Forty-two features are extracted for analysis, including ten time domain features, sixteen frequency domain features and sixteen wavelet packet energy features. Principal component analysis is used for feature reduction. Radial basis function neural network is adopted for classification and recognition. Flip chips in three states (good flip chips, flip chips with missing solder bumps and flip chips with open solder bumps) are utilized to validate the proposed method. The results demonstrate that this method is effective for defect inspection in flip chip package.

Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings

A novel hierarchical diagnosis network (HDN) is proposed by collecting deep belief networks (DBNs) by layer for the hierarchical identification of mechanical system. The deeper layer in HDN presents a more detailed classification of the result generated from the last layer to provide representative features for different tasks. A two-layer HDN is designed for a two-stage diagnosis with the wavelet packet energy feature. The first layer is intended to identify fault types, while the second layer is developed to further recognize fault severity ranking from the result of the first layer. To confirm the effectiveness of HDN, two similar networks constructed by support vector machine and back propagation neuron networks (BPNN) are employed to present a comprehensive comparison. The experimental results show that HDN is highly reliable for precise multi-stage diagnosis and can overcome the overlapping problem caused by noise and other disturbances.