This paper describes a d-q transformation to be applied to double-star permanent-magnet synchronous machines with interior magnets. The d-q model consists of two d-q reference frames decoupled with respect to each other and applies to a generic angular displacement between the winding sets. The decoupled d-q model simplifies the analysis of the machine behavior and the drive control implementation. Machine parameters are determined by a sequence of finite-element (FE) analyses. The effectiveness of the proposed modeling method is verified by a comparison with the results related to measurements on a test machine and FE simulations. It was assumed that the mutual inductances between the two winding sets can be represented with two coefficients. Although such an assumption inevitably leads to inaccuracy in the machine model, the simulation results with the proposed d-q model agree remarkably well with the experimental results and FE simulations.

This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.

This study introduces a novel approach for fault classification in bearing components utilizing raw accelerometer data. By employing various neural network models, including deep learning architectures, we bypass the traditional preprocessing and feature-extraction stages, streamlining the classification process. Utilizing the Case Western Reserve University (CWRU) bearing dataset, our methodology demonstrates remarkable accuracy, particularly in deep learning networks such as the three variant convolutional neural networks (CNNs), achieving above 98% accuracy across various loading levels, establishing a new benchmark in fault-detection efficiency. Notably, data exploration through principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) provided valuable insights into feature relationships and patterns, aiding in effective fault detection. This research not only proves the efficacy of neural network classifiers in handling raw data but also opens avenues for more straightforward yet effective diagnostic methods in machinery health monitoring. These findings suggest significant potential for real-world applications, offering a faster yet reliable alternative to conventional fault-classification techniques.

In this paper, an adaptation of a shaded pole motor operating as a single-phase brushless PM motor is studied. After properly designing the rotor PM ring to avoid demagnetization problems, a nonlinear mathematical model for performance assessment and design improvement is proposed. The calculation of the model parameters is carried out by 2-D FEM analyses suitably corrected to also take into account 3-D effects, mainly due to different stator and rotor lengths. The procedure implemented in a numerical code makes it possible to investigate significant design aspects and determine the mechanical characteristic related to motor configurations adopted for small fan appliances.

This paper deals with the power conversion system architectures to interface a stationary electrochemical storage installation with the network. Theoretical justifications about the conversion system layouts and control, used for actual Italian installations, are given. This paper aims at giving the power energy society an overview of actual possibilities of static conversion of d.c. battery sources.