In this paper, a new open-switch FAULT DIAGNOSIS method is proposed for the six-phase AC-DC converter based on the difference between the phase current and the corresponding reference using an adaptive threshold. The open-switch FAULTs are detected without any additional equipment and complicated calculations, since the proposed FAULT detection method is integrated with the controller required signals. The proposed FAULT-tolerant technique reduces the value of overcurrent and total harmonic distortion (THD) on the healthy and FAULTy phases, by considering the redundancy mode of space vectors in space vector pulse width modulation (SVPWM) and changing the switching signals in FAULT regions. This technique is performed without adding any legs, switches or triode for alternating currents (TRIAC) to the circuit. Finally, the proposed FAULT-tolerant technique is evaluated by MATLAB simulation and the results show its effectiveness.

Prompt detection and DIAGNOSIS of FAULTs in industrial processes are essential to minimize the production losses and increase the safety of the operator and the equipment. Several techniques are available in the literature to achieve these objectives. Neural networks are increasingly employed for FAULT DIAGNOSIS. This paper describes the application of different structures of neural networks like Self–Organizing Map neural network (SOM), Back Propagation (BP) and Radial Basis Function neural networks) RBF) for detecting FAULTs of a deaerator in the thermal power plant unit. Eight types of FAULTs are introduced in the simulated system and detected using these networks. The relative merits and demerits of using the three different structures are also discussed.

This paper concentrates on a new procedure which experimentally recognizes gears and bearings FAULTs of a typical gearbox system using a least square support vector machine (LSSVM). Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. The FAULT DIAGNOSIS method consists of three steps, firstly the six different base wavelets are considered. Out of these six wavelets, the base wavelet is selected based on wavelet selection criterion to extract statistical features from wavelet coefficients of raw vibration signals. Based on wavelet selection criterion, Daubechies wavelet and Meyer are selected as the best base wavelet among the other wavelets considered from the Maximum Relative Energy and Maximum Energy to Shannon Entropy criteria respectively. Finally, the gearbox FAULTs are classified using these statistical features as input to LSSVM technique. The optimal decomposition level of wavelet is selected based on the Maximum Energy to Shannon Entropy ratio criteria. In addition to this, Energy and Shannon Entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. Some kernel functions and multi kernel function as a new method are used with three strategies for multi classification of gearboxes. The results of FAULT classification demonstrate that the LSSVM identified the FAULT categories of gearbox more accurately with multi kernel and OAOT strategy.

Generators failure and generators break down can cause high financial consequences. For this reason, several concepts for the condition monitoring of generators have been developed. The purpose of this article is to collect significant generator data in order to form a comprehensive analysis by analytical hierarchy process (AHP) method. The AHP is a multi-criteria analysis approach, where is used in order to combine the results of online diagnostic methods and draw conclusions on the most probable failure. A FAULT DIAGNOSIS structure has been designed and several comparison charts have been generated. This trend led to the running generator probable failure, to be revealed. In this paper the circumstances of design and implementation of the LEMS structure with two simulation results are described.

One of the methods used to minimize the cost of maintaining and repairing rotating industrial equipment is condition monitoring by sound analysis. This study was performed to diagnose the FAULT of a single-phase electromotor through a machine learning method aiming to monitor its condition by sound analysis. Test conditions included healthy mode, bearing failure, shaft imbalance and shaft wear at two speeds of 500 and 1400 rpm. A microphone was installed on the electromotor to record data. After data collection, signal processing and statistical analysis, the data were clustered by machine learning method and K mean algorithm and the best characteristics were selected by PCA method. These features were used in the ANFIS modeling process. These features were common to both electromotor speeds. After evaluating the models, the best model had the highest accuracy value of 96. 82%. The average accuracy was 96. 71% for overall FAULT classification. The results showed that the analysis of acoustic signals and modeling process can be used to diagnose electromotor defects by machine learning method. Based on the obtained results, condition monitoring of the electromotor through acoustic analysis reduces its stop and continues its work process in the industry. The repair costs of the electromotor are reduced by its proper condition monitoring.

Machine learning enhances machine diagnostics through advanced data analysis, pattern recognition, and FAULT prediction. This study investigates the application of machine learning algorithms for bearing FAULT detection. The objective is to develop intelligent methodologies for the predictive DIAGNOSIS of bearing FAULTs in rotating machinery, emphasizing the significance of timely intervention to prevent critical failures. The methodology employed encompasses a systematic approach, including data preprocessing, feature extraction, and model development. This research employs advanced machine learning techniques, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Naive Bayes algorithms, in conjunction with time-domain and frequency-domain feature extraction methods. The implemented approach substantially enhances FAULT detection accuracy, achieving an aggregate classification precision of 97. 8% across all FAULT categories. Notably, the SVM algorithm demonstrates exceptional performance, attaining a 99. 2% accuracy rate in inner-race FAULT identification. This investigation provides a comprehensive analysis of the Case Western Reserve University (CWRU) dataset, data preprocessing procedures, feature extraction techniques, and machine learning algorithms utilized for FAULT detection. The results emphasize the effectiveness of these algorithms in bearing FAULT DIAGNOSIS, offering valuable insights for predictive maintenance strategies in industrial applications. This research also aligns with the objectives of Industry 4. 0, which focuses on utilizing intelligent, automated systems to enhance factory efficiency and reliability. The study concludes by proposing future research directions to further advance these technologies and support the transition toward more intelligent, interconnected industries.

FAULT DIAGNOSIS of rotary machines plays an essential role in reliability and safety of new industrial systems. Gears are considered as a vital part of the components of industrial machines, so that the defects of these components cause irreparable damages in industrial processes. Nowadays, many research workers conduct studies on the DIAGNOSIS of gear FAULTs using data analysis. In this research, to acquire acoustic data from a sample gearbox, a system was fabricated and developed. Then, some common FAULTs in the gearbox teeth were created artificially. In this research, cepstrum analysis method was used in order to detect the harmonics of gear mesh frequency and the family of sidebands created. In the primary investigation, the harmonics related to the gearbox shaft were identified with the cepstrum analysis method in the interval of 0 to 0. 25 seconds. Then, in order to detect the FAULTs of the gear, by analyzing in the interval of 0 to 0. 0002 seconds, the FAULTs related to the tooth were clearly visible and tracked. According to this research results by observing increase in amplitude of the first and fifth rahmonics, it is possible to detect FAULTs such as broken and worn teeth of gears. The obtained results show the effectiveness of the presented method to diagnose the FAULT in the gearbox and prevent unexpected costs.