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Reports how the theorem of the texture “tuned” mask was modified to solve some problems encountered in the automatic faults (including filling bars, oil stains, weft‐lacking and holes) detection and recognition of the plain woven fabrics. These problems are the faults of variable shapes and sizes, those of variable structure and the grey‐level differences in the faults of oil stains. The index of the “tuned” mask in the texture “tuned” mask theorem was modified to converge the variability of the faults, and to elongate the distances between each fault’s average texture energy so that the texture energy in normal texture and in faults can be confined to different fixed ranges. The results show that the optimum texture “tuned” mask found from the modified theorem of the texture “tuned” mask can be used satisfactorily to identify different faults due to structure, shapes and size variation. However, in the case of undertoned oil stains and lower density filling bars, this method may sometimes cause misidentification.

The purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.

The algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.

The first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.

Data points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.

The paper presents the application of self‐organizing neural network for the location of the fault in the transmission line and estimation of the parameter of the faulty element. The location of fault is done on the basis of the measurement of some node voltages of the line and appropriate preprocessing it to enhance the differences between different faults. The hybrid neural network is used to solve the problem. The self‐organizing layer of this network is used as the classifier. The output postprocessing MLP structure realizes the association of the place of the fault and its parameter with the measured set of node voltages. The results of computer experiments are given in the paper and discussed.

The purpose of this paper is to present a data fusion methodology for online oil condition and wear particles monitoring for assessment of a mechanical spur gear transmission system.

In this work, a background understanding of the tribological phenomena behind oil degradation and wear on the contact surface of mechanical elements is presented. Experimental results were obtained from oil continuously sampled from an operating a single-stage gearbox. Sampling was done by a multi-sensor automated prototype and online analysis performed by algorithms implemented in a C-code programmed graphical user interface.

Two sets of experiments were performed to observe different fault events frequently occurred in an industrial environment. Fault detection was achieved in appropriate time under constant operating conditions. Under variable operating conditions, same results were obtained by adjusting analysis parameters to critical operation conditions.

The value of this research work is the integration of the hardware and software necessary for online monitoring of oil condition and mechanical wear. The setup integrates online sampling with data acquisition, wireless communication, change detection and fault recognition computation. The approach has application in non-destructive online condition-based maintenance.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

The purpose of this paper is to apply grey statistical model to identify and classify live fault rupture.

Based on grey statistical mode, this paper uses eight faults' ripping speed observation data from 1997 to 2001, according to the grey statistics method for analysis, and recognizes active fault rupture situation. Using the conventional methods, namely taking all faults monitoring stations' average dislocation rate to analysis and make judgment, the average results are obtained.

The results show that the results are closer to reality because the grey statistical evaluation method has considered dislocation rate and other discrete factors.

The method exposed in the paper can be used to monitor and recognize live fault rupture in earthquake prediction.

According to the fault dislocation rate, this paper advances active fault rupture identification and classification method based on grey statistical model.

The purpose is using generative adversarial network (GAN) to solve the problem of sample augmentation in the case of imbalanced bearing fault data sets and improving residual network is used to improve the diagnostic accuracy of the bearing fault intelligent diagnosis model in the environment of high signal noise.

A bearing vibration data generation model based on conditional GAN (CGAN) framework is proposed. The method generates data based on the adversarial mechanism of GANs and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

The method proposed in this paper is verified by the western reserve data set and the truck bearing test bench data set, proving that the CGAN-based data generation method can form a high-quality augmented data set, while the CGAN-based and improved residual with attention mechanism. The diagnostic model of the network has better diagnostic accuracy under low signal-to-noise ratio samples.

A bearing vibration data generation model based on CGAN framework is proposed. The method generates data based on the adversarial mechanism of GAN and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

This research paper aims to investigate the change detection filter technique with a decision tree-based event (fault type) classifier for recognizing and categorizing power system disturbances on the high-voltage DC (HVDC) transmission link.

A change detection filter is used to the average and differential current components, which detects the point of fault initiation and records a change detection point (CDP). The half-cycle differential and average currents on both sides of the CDP are sent through the signal processing unit, which produces the respective target. The extracted target indices are sent through a decision tree-based fault classifier mechanism for fault classification.

In comparison with conventional differential current protection systems, the developed framework is faster in fault detection and classification and provides great accuracy. The new technology allows for prompt identification of the fault category, allowing electrical grids to be restored as quickly as possible to minimize economic losses. This novel technology enhances efficiency in terms of reducing computing complexity.

Setting a threshold value for identification is one of the limitations. To bring the designed system into stability condition before creating faults on it is another limitation. Reducing the computational burden is one of the limitations.

Creating a practical system in laboratory is difficult as it is a HVDC transmission line. Apart from that, installing rectifier and converter section for HVDC transmission line is difficult in a laboratory setting.

The suggested scheme’s importance and accuracy have been rigorously validated for the standard HVDC transmission system, subjected to various types of DC fault, and the results show the proposed algorithm would be a feasible alternative to real-time applications.

Process monitoring is a way to manage the quality characteristics of products in manufacturing processes. Several process monitoring based on machine learning algorithms have been proposed in the literature and have gained the attention of many researchers. In this paper, the authors developed machine learning-based control charts for monitoring fraction non-conforming products in smart manufacturing. This study proposed a relevance vector machine using Bayesian sparse kernel optimized by differential evolution algorithm for efficient monitoring in manufacturing.

A new approach was carried out about data analysis, modelling and monitoring in the manufacturing industry. This study developed a relevance vector machine using Bayesian sparse kernel technique to improve the support vector machine used to both regression and classification problems. The authors compared the performance of proposed relevance vector machine with other machine learning algorithms, such as support vector machine, artificial neural network and beta regression model. The proposed approach was evaluated by different shift scenarios of average run length using Monte Carlo simulation.

The authors analyse a real case study in a manufacturing company, based on best machine learning algorithms. The results indicate that proposed relevance vector machine-based process monitoring are excellent quality tools for monitoring defective products in manufacturing process. A comparative analysis with four machine learning models is used to evaluate the performance of the proposed approach. The relevance vector machine has slightly better performance than support vector machine, artificial neural network and beta models.

This research is different from the others by providing approaches for monitoring defective products. Machine learning-based control charts are used to monitor product failures in smart manufacturing process. Besides, the key contribution of this study is to develop different models for fault detection and to identify any change point in the manufacturing process. Moreover, the authors’ research indicates that machine learning models are adequate tools for the modelling and monitoring of the fraction non-conforming product in the industrial process.

Considering the problem that the high recognition rate of deep learning requires the support of mass data, this study aims to propose an insulating fault identification method based on small data set convolutional neural network (CNN).

Because of the chaotic characteristics of partial discharge (PD) signals, the equivalent transformation of the PD signal of unit power frequency period is carried out by phase space reconstruction to derive the chaotic features. At the same time, geometric, fractal, entropy and time domain features are extracted to increase the volume of feature data. Finally, the combined features are constructed and imported into CNN to complete PD recognition.

The results of the case study show that the proposed method can realize the PD recognition of small data set and make up for the shortcomings of the methods based on CNN. Also, the 1-CNN built in this paper has better recognition performance for four typical insulation faults of cable accessories. The recognition performance is improved by 4.37% and 1.25%, respectively, compared with similar methods based on support vector machine and BPNN.

In this paper, a method of insulation fault recognition based on CNN with small data set is proposed, which can solve the difficulty to realize insulation fault recognition of cable accessories and deep data mining because of insufficient measure data.