Methods of condition monitoring and fault detection for electrical machines

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Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines

Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from scheduled maintenance towards predictive maintenance, there is a significant lack of algorithms related to fault prediction of electrical machines. There is quite a lot of research going on in this area, but it is still underdeveloped and needs a lot more work. This paper presents a signal spectrum-based machine learning approach toward the fault prediction of electrical machines. The proposed method is a new approach to the predictive maintenance of electrical machines. This paper presents the details regarding the algorithm and then validates the accuracy against data collected from working electrical machines for both cases. A comparison is also presented at the end of multiple machine learning algorithms used for training based on this approach.Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical MachinespublishedVersio

Fault Detection and Predictive Maintenance of Electrical Machines

Nowadays, most domestic and industrial fields are moving toward Industry 4.0 standards and integration with information technology. To decrease shutdown costs and minimize downtime, manufacturers switch their production to predictive maintenance. Algorithms based on machine learning can be used to make predictions and detect timely potential faults in modern energy systems. For this, trained models with the usage of data analysis, cloud, and edge computing are implemented. The main challenge is the amount and quality of the data used for model training. This chapter discusses a specific version of a condition monitoring system, including maintenance approaches and machine learning algorithms and their general application issues

Magnetic Equivalent Circuit and Lagrange Interpolation Function Modeling of Induction Machines Under Broken Bar Faults

This paper introduces a mesh-based magnetic equivalent circuit (MEC) modeling technique for induction machines (IMs) in healthy and broken rotor bars conditions. The MEC model is presented as a highly accurate and computationally efficient alternative to finite element (FE) models. By incorporating modifications to the air gap coupling method, including a new Lagrange interpolation function, and utilizing a harmonic MEC model, the accuracy of the solution is improved while reducing electrical and mechanical transients. Compared to experiments and 2D FE models, this model achieves precise results for electromagnetic torque, rotational speed, and forces across various conditions. The Lagrange interpolation function forms the basis for the air gap coupling between stator and rotor flux densities. The results demonstrate the MEC model’s exceptional accuracy in predicting speed oscillations, calculating forces, and analyzing current harmonics in faulty IMs. Furthermore, the MEC model performs over 30 times faster than the 2D FE models.acceptedVersionPeer reviewe

Methods of Condition Monitoring and Fault Detection for Electrical Machines

Nowadays, electrical machines and drive systems are playing an essential role in different applications. Eventually, various failures occur in long-term continuous operation. Due to the increased influence of such devices on industry, industrial branches, as well as ordinary human life, condition monitoring and timely fault diagnostics have gained a reasonable importance. In this review article, there are studied different diagnostic techniques that can be used for algorithms’ training and realization of predictive maintenance. Benefits and drawbacks of intelligent diagnostic techniques are highlighted. The most widespread faults of electrical machines are discussed as well as techniques for parameters’ monitoring are introduced

Fault Detecting Accuracy of Mechanical Damages in Rolling Bearings

Electrical machines are to face different challenging factors during operation, such as high unexpected or excessive loads, unusual properties of the working environment, or intense fluctuations in rotation speed. Therefore, maintenance questions and predicting the accuracy of an equipment’s condition have great importance. This study is based on the theory of vibration reliability. This article introduces the most common faults of bearings in electrical machines and discusses their diagnostic possibilities. Experimental setup, as well as studied bearing failures, are described. The accuracy of conducted experiments is introduced

The Bearing Faults Detection Methods for Electrical Machines—The State of the Art

Electrical machines are prone to faults and failures and demand incessant monitoring for their confined and reliable operations. A failure in electrical machines may cause unexpected interruptions and require a timely inspection of abnormal conditions in rotating electric machines. This article aims to summarize an up-to-date overview of all types of bearing faults diagnostic techniques by subdividing them into different categories. Different fault detection and diagnosis (FDD) techniques are discussed briefly for prognosis of numerous bearing faults that frequently occur in rotating machines. Conventional approaches, statistical approaches, and artificial intelligence-based architectures such as machine learning and deep learning are discussed summarily for the diagnosis of bearing faults that frequently arise in revolving electrical machines. The most advanced trends for diagnoses of frequent bearing faults based on intelligence and novel applications are reviewed. Future research directions that are helpful to enhance the performance of conventional, statistical, and artificial intelligence (machine learning, deep learning) and novel approaches are well addressed and provide hints for future work

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes

Parametric Digital Twin of autonomous electric vehicle transmission

Variable applications and methodologies are used in the Digital Twin technology. Digital Twin as a trending technology is also a general topic of many industry-oriented research projects. To develop and implement a novel technology, a detailed study of any single part of a system is required. This paper presents a development case study of the parametric Digital Twin of autonomous electric vehicle transmission. Digital Twin combines the advantages of software models and real equipment to reduce total test runs and safe maintenance. The primary duty of the Digital Twin is to allow complete synchronization and connectivity between virtual and real entities. The paper presents a detailed structural description of the virtual entity that considers the parametrization of the transmission