Fault Identification Model Research Articles

Proposed Model for Remote Maintenance of CCTV Cameras

A data center is a physical facility in which an organization stores its critical applications and data. In our paper, the researchers developed an Android model for surveillance CCTV system maintenance and fault identification. The descriptive analytic approach and object-oriented systems analysis and design approach were used. The Unified Modeling Language (UML) was used as a tool in analysis and design, and Java was used as a programming language to develop an Android model to maintain CCTV faults. Image freezing, incorrect time and date, and video delay were resolved. It has been developed an Android model that works on smart phones to monitor and maintain surveillance CCTV systems. This model allows users to view faults reports and fix camera faults, such as (image freezing, camera image delay, and incorrect time on the camera) in surveillance CCTV systems from anywhere that has Internet access.

Unmanned aerial vehicle rotor fault diagnosis based on interval sampling reconstruction of vibration signals and a one-dimensional convolutional neural network deep learning method

With the aim of identifying possible mechanical faults in unmanned aerial vehicle (UAV) rotors during operation, this paper proposes a method based on interval sampling reconstruction of vibration signals and one-dimensional convolutional neural network (1D-CNN) deep learning. Firstly, experiments were designed to collect the vibration acceleration signals of a UAV working at high speed under three states (normal, rotor damage by varying degrees, and rotor crack by different degrees). Then, considering the powerful feature extraction and complex data analysis abilities of 1D-CNN, an effective deep learning model for fault identification is established utilizing 1D-CNN. During analysis, it is found that the recognition rate for minor faults is not ideal, with all weak states being identified as normal, reducing the overall identification accuracy, when using conventional sequential sampling to construct learning sample sets. To this end, in order to make the sample data cover the whole process of data collection as much as possible, a learning sample processing method based on interval sampling reconstruction of the vibration signal is proposed. And it is also verified that the reconstructed sample set can easily reflect the global information of mechanical operation. Finally, according to the comparison of analysis results, the recognition rate of the deep learning model for different degrees of faults is greatly improved, and minor faults could also be accurately identified through this method. The results show that the 1D-CNN deep learning model could diagnose and identify UAV rotor damage faults accurately, by incorporating the proposed method of interval sampling reconstruction.

Core looseness fault identification model based on Mel spectrogram-CNN

In order to realize transformer voiceprint recognition, a transformer voiceprint recognition model based on Mel spectrum convolution neural network is proposed. Firstly, the transformer core looseness fault is simulated by setting different preloads, and the sound signals under different preloads are collected; Secondly, the sound signal is converted into a spectrogram that can be trained by convolutional neural network, and then the dimension is reduced by Mel filter bank to draw Mel spectrogram, which can generate spectrogram data sets under different preloads in batch; Finally, the data set is introduced into convolutional neural network for training, and the transformer voiceprint fault recognition model is obtained. The results show that the training accuracy of the proposed Mel spectrum convolution neural network transformer identification model is 99.91%, which can well identify the core loosening faults.

A Probabilistic Calculation Method of Transformer Temperature Fault Based on Photoelectric Infrared Sensor

Aiming at the defect of the lack of quantitative detection probability evaluation method, we design two models which affect the detection probability of image fusion system. They are spectral contrast modeling of target and background and probability model of transformer fault detection and recognition under the influence of multiple factors. Based on the infrared optical imaging method, we established the fault identification model of multi-mode transformer or its connected parts; Studied several infrared optical imaging fusion test systems, and deduced the calculation method of transformer fault detection based on false alarm probability; Analyzed the correlation between the detection ability of the detection system and the characteristics of the target itself, environmental illumination, detection distance and so on. According to the relationship among SNR, detection range and detection probability, we derived the calculation model of target detection probability of optical imaging system under complex background. Through calculation and experimental analysis, the results show that the proposed method can effectively evaluate the detection probability of photoelectric detection imaging system under different background in practical application.

On‐Line Fault Diagnosis Model of Distribution Transformer Based on Parallel Big Data Stream and Transfer Learning

Aiming at the problem of calculation speed for distribution transformer on‐line fault diagnosis, and the shortage of on‐line monitoring type and routine test data, an on‐line fault diagnosis model of distribution transformer based on parallel big data stream and transfer learning is established. First, the fault identification feasibility based on the present on‐line monitoring data of distribution transformer is analyzed, and the main indicators of on‐line fault identification are proposed. Second, the on‐line fault identification method based on ARIMA is proposed, on this basis, the on‐line fault identification model of distribution transformer based on big data stream is established, and the distribution transformers that possess hidden fault are selected, which would be diagnosed further. In order to improve the efficiency, the model is completed on Storm platform in parallel form. Then, the distribution transformer fault diagnosis indicator system is construct. In order to achieve fault diagnosis of the above selected distribution transformers, the effective fault information from other distribution transformers is extracted using the transfer learning algorithm TrAdaBoost, which is used as auxiliary data for the fault diagnosis tool training of the distribution transformer to be diagnosed. At the same time, the model is completed on Storm platform to improve the efficiency. Finally, based on the distribution transformer fault data, the distribution transformer fault diagnosis is simulated, and results show that the fault diagnosis is accurate and efficient. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Recognition of Fault State of RV Reducer Based on self-organizing feature map Neural Network

In order to accurately evaluate the working state of RV reducer, a fault identification method based on the fault identification model established by Self-Organizing Feature Map (SOM) Neural Network is proposed. Firstly, the data measured by the RV reducer test platform are analyzed by wavelet to obtain the wavelet coefficient. Then, combined with the efficiency data of RV reducer, the mean square frequency, center of gravity frequency and frequency variance of the two groups of data are calculated after Fourier transform and power spectrum analysis. After optimization, several eigenvalues are obtained. The eigenvalues are input into the competitive neural network and SOM neural network to establish the fault identification model. Finally, the results of the fault identification model established by the competitive neural network and SOM neural network are compared. The prediction results show that the fault identification model established by SOM neural network can effectively determine the working state of RV reducer.

Establishment and optimization of sensor fault identification model based on classification and regression tree and particle swarm optimization

The accuracy of structural state evaluation may be affected by the damaged piezoelectric sensors. Therefore, it is necessary to identify the sensor fault during monitoring. This paper proposes a method based on classification and regression tree (CART) and particle swarm optimization (PSO) to improve the efficiency of potential feature sets selection for sensor fault classification and build an identification model with the best performance. Firstly, the signal features of three structural changes and four sensor faults were extracted with five indexes. Then the decision trees (DT) for sensor fault classification were built based on different index combinations whose performances were then evaluated by the designed fitness function. Finally, PSO was used to optimize the searching for the best index combination. The results show that compared with the exhaustive method, adopting PSO for DT optimization can greatly simplify the search process. When the particle population is 5 and 10, the fitness converges to the optimal solution after only 6 and 4 iterations respectively. Although the DT with the best fitness is trained with only two indexes, its accuracy is higher than those trained with more indexes and the classification accuracy of 64 samples reaches 98.4% which shows the feasibility and practicability of the method.

Corrosion fault identification model of substation grounding grid based on PSO-LS-SVM

As one of the important equipment to ensure the normal operation of substation, the performance of grounding grid has been highly concerned. In recent years, researchers propose that the theory of electromagnetic induction can be used to diagnose and identify the corrosion fault of substation grounding grid. In this paper, a fault classification model based on LS-SVM optimized by PSO is proposed to identify the corrosion fault of grounding grid. Firstly, the wavelet packet transform principle is used to filter the original data, and the wavelet packet energy is used to construct the fault eigenvalue as the input of the fault classification model. Based on LS-SVM, a corrosion fault classification model is constructed, and particle swarm optimization method is used to optimize the parameters of the model, which solves the problem of traditional SVM parameter optimization. Through the practical application in substation, the model proposed in this paper can identify the corrosion of grounding grid conductor without excavation and power failure, which provides an effective scheme for engineering application.

Application of Deep Learning in Photovoltaic Array Fault Identification

If a photovoltaic power station needs to operate normally, it cannot do without the blessing of the photovoltaic array, which also takes up about half of the cost of the photovoltaic system. The failure and low efficiency of photovoltaic power plants are the main two reasons for the loss of energy. Among them, the failure of photovoltaic arrays is undoubtedly one of the important reasons. In this article, the author uses the superiority of deep neural network in fault identification technology to complete the fault identification model of photovoltaic array.

Research on Fault Diagnosis Method of Electro-Hydrostatic Actuator

Electrohydrostatic actuator is a type of actuator that uses hydraulic energy as the energy transmission carrier, which has the advantages of small size and high power. Since it is commonly used in harsh conditions such as strong vibration, high pressure, and heavy loads, condition monitoring and fault diagnosis of its hydraulic system are particularly important. This paper proposed a novel fault feature extraction method and applied to fault diagnosis of electrohydrostatic actuator. Firstly, the pressure signal of the hydraulic system is decomposed at multiple scales to obtain the center frequency of its maximum energy intrinsic mode component, and the feature data set is constructed based on the statistical features of the time domain. Then, a fault identification model of hydraulic system based on support vector machine is established. Finally, the fault classification and identification results of the hydraulic system are outputted. After a variety of method comparisons, the method proposed in this paper has a fault time ratio accuracy of 96.7%, which provides a basis and a new way for the fault diagnosis of the hydraulic system.

Faulty Line Identification Method Based on Bayesian Optimization for Distribution Network

Selective ground-fault protection is greatly valued for the safe and reliable operation of power systems. With the wide adoption of fault indicator in distribution network, the amount of available fault data increases dramatically. The in-depth investigation of fault recording data helps improve the accuracy of faulty line identification. To perform fault data analysis with higher efficiency, a single-phase-to-ground fault identification model based on the k-Nearest Neighbor (kNN) classification algorithm is proposed in the paper. In this model, the eigenvectors consist of wavelet energy ratio, wavelet coefficients variance and wavelet power obtained by the decomposition of transient components. Furthermore, through the theoretical analysis and experimental comparison of three parameter adjustment algorithms, Bayesian Optimization algorithm is selected to find the optimal parameters of fault identification model, and realize the adaptive adjustment of model parameters. Finally, the validity and feasibility of the model are verified by the experimental data, and the accuracy and efficiency of fault identification are improved by using Bayesian Optimization algorithm.

An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device

Considering that the vibration signals of gears and bearings in the automatic transmission device are complex and the fault features are difficult to extract. This paper proposes a method for extracting fault features of transmission device using adaptive variational modal decomposition (AVMD), and uses deep belief network (DBN) for pattern recognition. The vibration signal is decomposed by AVMD using energy ratio method. The intrinsic mode function (IMF) with abundant fault information is obtained. By calculating the energy entropy of each IMF component and form a high-dimensional feature vector as the input of DBN to establish an early fault identification model. The early fault data of the PHM2009 transmission device experimental platform was selected for identification and analysis. The identification results show that AVMD can extract the weak features of transmission device fault signals more accurately than empirical mode decomposition (EMD). Moreover, DBN has a higher fault identification accuracy rate than support vector machine (SVM), probabilistic neural network (PNN), back propagation neural network (BP) and Kohonen self-organizing competition neural network.

A Method for Constructing Automatic Rolling Bearing Fault Identification Model Based on Refined Composite Multi-Scale Dispersion Entropy

In this paper, one of most widely utilized rolling bearings in rotating machinery is selected as the research object. Automatic rolling bearing fault identification model including support vector machine (SVM) training module, fault classification knowledge base module, and fault automatic identification module is proposed. A generalized method for automatic identification of rolling bearing faults based on refined composite multi-scale dispersion entropy (RCMDE) is developed. First, in order to solve the problem of setting the value range of the decomposition level K based on empirical knowledge for variational modal decomposition (VMD), a maximum kurtosis value method is proposed to determine the preset value range of K in whale optimization algorithm. Then, an improved VMD method is used to adaptively decompose the signal into a series of intrinsic mode function components. Next, the correlation coefficient method is employed to screen effective feature components of bearings in different health states for reconstruction. Through theoretical analysis, the calculated RCMDE value of reconstructed signal is screened and input as a feature value into the optimized SVM classifier for fault pattern recognition. The input of rolling bearing vibration data without preprocessing and the output of the fault identification which don't rely on empirical knowledge of external experts is realized. Experimental and engineering case data of rolling bearings under different equipment and operating environments are tested and validated. The results indicate that the model proposed in this paper shows good fault identification, demonstrates good generalization performance, and has beneficial industrial application prospect.

Novel Orbit-based CNN Model for Automatic Fault Identification of Rotating Machines

Various faults in high-fidelity turbomachinery such as steam turbines and centrifugal compressors usually result in unplanned outage thus lowering the reliability and productivity while largely increasing the maintenance costs. Condition monitoring has been increasingly applied to provide early alerting on component faults by using the vibration signals. However, each type of fault in different types of rotating machines usually require an individual model to isolate the damage for accurate condition monitoring, which require costly computation efforts and resources due to the data uncertainties and modeling complexity. This paper presents a generalized deep learning methodology for accurately automatic diagnostics of various faults in general rotating machines by utilizing the shaft orbits generated from vibration signals, considering the high non-linearity and uncertainty of the sensed vibration signals. The sensor anomalies and environmental noise in the vibration signals are first addressed through waveform compensation and Bayesian wavelet noise reduction filtering. Shaft orbit images are generated from the cleansed vibration data collected from different turbomachinery with various fault modes. A multi-layer convolutional neural network model is then developed to classify and identify the shaft orbit images of each fault. Finally, the fault diagnosis of rotating machinery is realized through the automated identification process. The proposed approach retains the fault information in the axis trajectory to the greatest extent, and can adeptly extract and accurately identify features of various faults. The effectiveness and feasibility of the proposed methodology is demonstrated by using the sensed vibration signals collected from real-world centrifugal compressors and steam turbines with different fault modes.

A novel unsupervised learning method for intelligent fault diagnosis of rolling element bearings based on deep functional auto-encoder

Recently, several studies tried to develop fault identification models for rolling element bearing based on unsupervised learning techniques. However, an accurate intelligent fault diagnosis system is still a big challenge. In this study, a deep functional auto-encoders (DFAEs) model with SoftMax classifier was designed for valuable feature extraction from massive raw vibration signals. To maximize the unsupervised feature learning ability of the proposed model, various activation functions were applied in an effective methodology, these hidden activation functions enhance significantly the sparsity of the training data-set. The proposed method was validated using the raw vibration signals measured from the machine with different bearing conditions. The achieved results showed that the high-superiority of the proposed model comparing to standard deep learning and other traditional fault diagnosis methods in terms of classification accuracy even with massive input data sets.

Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting

In order to improve the accuracy of fault diagnosis on wind turbines, this paper presents a method of wind turbine fault diagnosis based on ReliefF algorithm and eXtreme Gradient Boosting (XGBoost) algorithm by using the data in supervisory control and data acquisition (SCADA) system. The algorithm consists of the following two parts: The first part is the ReliefF multi-classification feature selection algorithm. According to the SCADA history data and the wind turbines fault record, the ReliefF algorithm is used to select feature parameters that are highly correlated with common faults. The second part is the XGBoost fault recognition algorithm. First of all, we use the historical data records as the input, and use the ReliefF algorithm to select the SCADA system observation features with high correlation with the fault classification, then use these feature data to build the XGBoost multi classification fault identification model, and finally we input the monitoring data generated by the actual running wind turbine into the XGBoost model to get the operation status of the wind turbine. We compared the algorithm proposed in this paper with other algorithms, such as radial basis function-Support Vector Machine (rbf-SVM) and Adaptive Boosting (AdaBoost) classification algorithms, and the results showed that the classification accuracy using “ReliefF + XGBoost” algorithm was higher than other algorithms.

A novel intelligent fault identification method based on random forests for HVDC transmission lines.

In order to remedy the current problem of having been buffeted by competing requirements for both protection sensitivity and quick reaction of High Voltage Direct Current (HVDC) transmission lines simultaneously, a new intelligent fault identification method based on Random Forests (RF) for HVDC transmission lines is proposed. S transform is implemented to extract fault current traveling wave of 8 frequencies and calculate the fluctuation index and energy sum ratio, in which the wave index is used to identify internal and external faults, and energy sum ratio is used to identify the positive and negative pole faults occurred on the transmission line. The intelligent fault identification model of RF is established, and the fault characteristic sample set of HVDC transmission lines is constructed by using multi-scale S transform fluctuation index and multi-scale S-transform energy sum ratio. Training and testing have been carried out to identify HVDC transmission line faults. According to theoretical researches and a large number of results of simulation experiments, the proposed intelligent fault identification method based on RF for HVDC transmission lines can effectively solve the problem of protection failure caused by inaccurate identification of traditional traveling wave wavefront or wavefront data loss. It can accurately and quickly realize the identification of internal and external faults and the selection of fault poles under different fault distances and transitional resistances, and has a strong ability to withstand transitional resistance and a strong ability to resist interference.

Research on Active Emergency Repair Technology of Distribution Network Based on Large Power Data

Distribution network emergency repair is the most important work in power supply service and production management. Active emergency repair is an important way to improve the efficiency of emergency repair command. In this paper, we investigate the outage data of private and public transformer in the electric power information collection system. By using data mining technology, we build the relationship between outage data and actual line faults. A real fault identification model based on the detailed outage data of private and public transformer is established, which can quickly and accurately identify real faults and facilitate maintenance personnel to take timely measures, and achieve active emergency repair.

Investigating the Construction, Training, and Verification Methods of k-Means Clustering Fault Recognition Model for Rotating Machinery

Considerable studies have been carried out in recent years regarding fault diagnosis and prediction for the rotating machinery in industrial plants. However, few works present the use of clustering approaches applied to time series to diagnose machine faults. With the increasing practical requirement of safety, reliability, availability and maintainability of machinery running, predictive maintenance based on the technologies of fault diagnosis and prediction has also received significant attention in recent years. In the present study, under Cyber-physical systems (CPS) condition, k-means clustering analysis based on the fault case big data machine learning is applied to investigate the fault identification of the rotating machinery without external expert support. K-means cluster-based fault identification model, which includes the k-means cluster analysis module, fault mode - fault cluster centroid knowledge base module and fault identification module has been constructed. Moreover, the fault feature extraction and fault eigenvectors screening are studied in detail. The vibration data of surge, rubbing, misalignment and normal status of the centrifugal compressor in industrial plants are utilized to train and verify the effectiveness of the k-means cluster fault recognition model. The obtained result shows that recognition accuracy rates of the surge, rubbing and misalignment faults reach 94%, 100% and 80%, respectively. However, the effectiveness of the cluster analysis of vibration data for five or more operating states should be studied in the future.

Implementation of Rule based Classifiers for Wind Turbine Blade Fault Diagnosis using Vibration Signals

The fast developing wind industry has revealed a requirement for more multifaceted fault diagnosis system in the segments of a wind turbine. “Present wind turbine researches concentrate on enhancing their dependability quality and decreasing the cost of energy production, especially when wind turbines are worked in off-shore places. Wind turbine blades are ought to be an important component among the other basic segments in the wind turbine framework since they transform dynamic energy of wind into useable power and due to environmental conditions, it get damage often and cause lack in productivity. The main objective of this study is to carry out a fault identification model for wind turbine blade using a machine learning approach through vibration data to classify the blade condition. Here five faults namely, blade bend, hub-blade loose connection, blade cracks, blade erosion and pitch angle twist have been considered. Machine learning approach has three steps namely feature extraction, feature selection and feature classification. Feature extraction was carried out by statistical analysis followed by feature selection using J48 decision tree algorithm. Feature classification was done using twelve rule based classifiers using WEKA. The results were compared with respect to the classification accuracy and the computational time of the classifier.”