A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms

This paper puts forward a real-time smart fault diagnosis system (SFDS) intended for high-speed protection of power system transmission lines. This system is based on advanced signal processing techniques, traveling wave theory results, and machine learning algorithms. The simulation results show that the SFDS can provide an accurate internal/external fault discrimination, fault inception time estimation, fault type identification, and fault location. This paper presents also the hardware requirements and software implementation of the SFDS

A multi-scale smart fault diagnosis model based on waveform length and autoregressive analysis for PV system maintenance strategies

Nonlinear photovoltaic (PV) output is greatly affected by the nonuniform distribution of daily irradiance, preventing conventional protection devices from reliably detecting faults. Smart fault diagnosis and good maintenance systems are essential for optimizing the overall productivity of a PV system and improving its life cycle. Hence, a multiscale smart fault diagnosis model for improved PV system maintenance strategies is proposed. This study focuses on diagnosing permanent faults (open-circuit faults, ground faults, and line-line faults) and temporary faults (partial shading) in PV arrays, using the random forest algorithm to conduct time-series analysis of waveform length and autoregression (RF-WLAR) as the main features, with 10-fold cross-validation using Matlab/Simulink. The actual irradiance data at 5.86 °N and 102.03 °E were used as inputs to produce simulated data that closely matched the on-site PV output data. Fault data from the maintenance database of a 2 MW PV power plant in Pasir Mas Kelantan, Malaysia, were used for field testing to verify the developed model. The RF-WLAR model achieved an average fault-type classification accuracy of 98 %, with 100% accuracy in classifying partial shading and line-line faults

Blind Application of Developed Smart Vibration-Based Machine Learning (SVML) Model for Machine Faults Diagnosis to Different Machine Conditions

From Springer Nature via Jisc Publications RouterHistory: received 2020-02-11, rev-recd 2020-08-21, accepted 2020-09-16, registration 2020-09-16, pub-electronic 2020-10-07, online 2020-10-07, pub-print 2021-06Publication status: PublishedFunder: University of ManchesterAbstract: Purpose: The development and application of intelligent models to perform vibration-based condition monitoring in industry seems to be receiving attention in recent years. A number of such research studies using the artificial intelligence, machine learning, pattern recognition, etc., are available in the literature on this topic. These studies essentially used the machine vibration responses with known machine faults to develop smart fault diagnosis models. These models are yet to be tested for all kinds of machine faults and/or different operating conditions. Therefore, the purpose is to develop a generic machine faults diagnosis model that can be applied blindly to any identical machines with high confidence level in accuracy of the predictions. Methods: In this paper, a supervised smart fault diagnosis model is developed. This model is developed using the available measured vibration responses for the different rotor faults simulated on an experimental rotating rig operating at a constant speed. The developed smart vibration-based machine learning (SVML) model is then blindly tested to identify the healthy and faulty conditions of the rig when operating at different speeds. Results and conclusions: Several scenarios are proposed and examined during the development of the SVML model. It is observed that scenario of the vibration measurements simultaneously from all bearings from a machine is capable to fully map the machine dynamics in the VML model. Therefore, this developed when applied blindly to the sets of data at a different machine speed, the results are observed to be encouraging. The results clearly show a possibility for a centralised vibration-based condition monitoring (CVCM) model for identical machines operating at different rotating speeds

Feature Extraction from Indirect Monitoring in Marine Oil Separation Systems

In this article, a study of characteristic vibrations of marine oils separation system is presented. Vibrations analysis allows for the extraction of representative features that could be related to the lifetime of their pieces. Actual measurements were carried out on these systems on Ro-Pax vessels to transport passengers and freight. The vibrations obtained were processed in the frequency domain and following this, they were used in a Genetic Neuro-Fuzzy System in order to design new predictive maintenance strategies. The obtained results show that these techniques as a promising strategy can be utilized to determine incipient faults.This work has been supported by the Spanish Government [MAQ-STATUS DPI2015-69325-C2] and [DPI2015-69 1808271602] of Ministerio de Economía y Competitividad and with European Funds of Regional Development (FEDER)

Design and Control of Electrical Motor Drives

Dear Colleagues, I am very happy to have this Special Issue of the journal Energies on the topic of Design and Control of Electrical Motor Drives published. Electrical motor drives are widely used in the industry, automation, transportation, and home appliances. Indeed, rolling mills, machine tools, high-speed trains, subway systems, elevators, electric vehicles, air conditioners, all depend on electrical motor drives.However, the production of effective and practical motors and drives requires flexibility in the regulation of current, torque, flux, acceleration, position, and speed. Without proper modeling, drive, and control, these motor drive systems cannot function effectively.To address these issues, we need to focus on the design, modeling, drive, and control of different types of motors, such as induction motors, permanent magnet synchronous motors, brushless DC motors, DC motors, synchronous reluctance motors, switched reluctance motors, flux-switching motors, linear motors, and step motors.Therefore, relevant research topics in this field of study include modeling electrical motor drives, both in transient and in steady-state, and designing control methods based on novel control strategies (e.g., PI controllers, fuzzy logic controllers, neural network controllers, predictive controllers, adaptive controllers, nonlinear controllers, etc.), with particular attention to transient responses, load disturbances, fault tolerance, and multi-motor drive techniques. This Special Issue include original contributions regarding recent developments and ideas in motor design, motor drive, and motor control. The topics include motor design, field-oriented control, torque control, reliability improvement, advanced controllers for motor drive systems, DSP-based sensorless motor drive systems, high-performance motor drive systems, high-efficiency motor drive systems, and practical applications of motor drive systems. I want to sincerely thank authors, reviewers, and staff members for their time and efforts. Prof. Dr. Tian-Hua Liu Guest Edito

From total quality management to Quality 4.0: a systematic literature review and future research agenda

Quality 4.0 is an emerging concept that has been increasingly appreciated because of the intensification of competition, continually changing customer requirements and technological evolution. It deals with aligning quality management practices with the emergent capabilities of Industry 4.0 to improve cost, time, and efficiency and increase product quality. This article aims to comprehensively review extant studies related to Quality 4.0 to uncover current research trends, distil key research topics, and identify areas for future research. Thus, 46 journal articles extracted from the Scopus database from 2017 to 2022 were collected and reviewed. A descriptive analysis was first performed according to the year-wise publication, sources of publication, and research methods. Then, the selected articles were analyzed and classified according to four research themes: Quality 4.0 concept, Quality 4.0 implementation, quality management in Quality 4.0, and Quality 4.0 model and application. By extracting the literature review findings, we identify the Quality 4.0 definitions and features, develop the quality curve theory, and highlight future research opportunities. This study supports practitioners, managers, and academicians in effectively recognizing and applying Quality 4.0 to enhance customer satisfaction, achieve innovation enterprise efficiency, and increase organizational competitiveness in the era of Industry 4.0.National Social Science Fund of China: Grant No. 21ZDA02

Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion

Safe and reliable operations of industrial machines are highly prioritized in industry. Typical industrial machines are complex systems, including electric motors, gearboxes and loads. A fault in critical industrial machines may lead to catastrophic failures, service interruptions and productivity losses, thus condition monitoring systems are necessary in such machines. The conventional condition monitoring or fault diagnosis systems using signal processing, time and frequency domain analysis of vibration or current signals are widely used in industry, requiring expensive and professional fault analysis team. Further, the traditional diagnosis methods mainly focus on single components in steady-state operations. Under dynamic operating conditions, the measured quantities are non-stationary, thus those methods cannot provide reliable diagnosis results for complex gearbox based powertrains, especially in multiple fault contexts. In this dissertation, four main research topics or problems in condition monitoring of gearboxes and powertrains have been identified, and novel solutions are provided based on data-driven approach. The first research problem focuses on bearing fault diagnosis at early stages and dynamic working conditions. The second problem is to increase the robustness of gearbox mixed fault diagnosis under noise conditions. Mixed fault diagnosis in variable speeds and loads has been considered as third problem. Finally, the limitation of labelled training or historical failure data in industry is identified as the main challenge for implementing data-driven algorithms. To address mentioned problems, this study aims to propose data-driven fault diagnosis schemes based on order tracking, unsupervised and supervised machine learning, and data fusion. All the proposed fault diagnosis schemes are tested with experimental data, and key features of the proposed solutions are highlighted with comparative studies.publishedVersio

An investigation into applying ontologies to the UK railway industry

The uptake of ontologies in the Semantic Web and Linked Data has proven their excellence in managing mass data. Referring to the movements of Linked Data, ontologies are applied to large complex systems to facilitate better data management. Some industries, e.g., oil and gas, have at-tempted to use ontologies to manage its internal data structure and man-agement. Researchers have dedicated to designing ontologies for the rail system, and they have discussed the potential benefits thereof. However, despite successful establishment in some industries and effort made from some research, plus the interest from major UK rail operation participants, there has not been evidence showing that rail ontologies are applied to the UK rail system. This thesis will analyse factors that hinder the application of rail ontolo-gies to the UK rail system. Based on concluded factors, the rest of the the-sis will present corresponding solutions. The demonstrations show how ontologies can fit in a particular task with improvements, aiming to pro-vide inspiration and insights for the future research into the application of ontology-based system in the UK rail system