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.

This paper presents current research trends and prospects of utilizing additive manufacturing (AM) techniques to manufacture electrical machines. Modern-day machine applications require extraordinary performance parameters such as high power-density, integrated functionalities, improved thermal, mechanical & electromagnetic properties. AM offers a higher degree of design flexibility to achieve these performance parameters, which is impossible to realize through conventional manufacturing techniques. AM has a lot to offer in every aspect of machine fabrication, such that from size/weight reduction to the realization of complex geometric designs. However, some practical limitations of existing AM techniques restrict their utilization in large scale production industry. The introduction of three-dimensional asymmetry in machine design is an aspect that can be exploited most with the prevalent level of research in AM. In order to take one step further towards the enablement of large-scale production of AM-built electrical machines, this paper also discusses some machine types which can best utilize existing developments in the field of AM.

Electrical machines are prone to various faults and require constant monitoring to ensure safe and dependable functioning. A potential fault in electrical machinery results in unscheduled downtime, necessitating the prompt assessment of any abnormal circumstances in rotating electrical machines. This paper provides an in-depth analysis as well as the most recent trends in the application of condition monitoring and fault detection techniques in the disciplines of electrical machinery. It first investigates the evolution of traditional monitoring techniques, followed by signal-based techniques such as spectrum, vibration, and temperature analysis, and the most recent trends in its signal processing techniques for assessing faults. Then, it investigates and details the implementation and evolution of modern approaches that employ intelligence-based techniques such as neural networks and support vector machines. All these applicable and state-of-art techniques in condition monitoring and fault diagnosis aid in predictive maintenance and identification and have the highly reliable operation of a motor drive system. Furthermore, this paper focuses on the possible transformational impact of electrical machine condition monitoring by thoroughly analyzing each of the monitoring techniques, their corresponding pros and cons, their approaches, and their applicability. It offers strong and useful insights into proactive maintenance measures, improved operating efficiency, and specific recommendations for future applications in the field of diagnostics.

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.

Additively manufactured soft magnetic Fe-3.7%w.t.Si toroidal samples with solid and novel partitioned cross-sectional geometries are characterized through magnetic measurements. This study focuses on the effect of air gaps and annealing temperature on AC core losses at the 50 Hz frequency. In addition, DC electromagnetic material properties are presented, showing comparable results to conventional and other 3D-printed, high-grade, soft magnetic materials. The magnetization of 1.5 T was achieved at 1800 A/m, exhibiting a maximum relative permeability of 28,900 and hysteresis losses of 0.61 (1 T) and 1.7 (1.5 T) W/kg. A clear trend of total core loss reduction at 50 Hz was observed in relation to the segregation of the specimen cross-sectional topology. The lowest 50 Hz total core losses were measured for the toroidal specimen with four internal air gaps annealed at 1200 °C, exhibiting a total core loss of 1.2 (1 T) and 5.5 (1.5 T) W/kg. This is equal to an 860% total core loss reduction at 1 T and a 510% loss reduction at 1.5 T magnetization compared to solid bulk-printed material. Based on the findings, the advantages and disadvantages of printed air-gapped material internal structures are discussed in detail.

Since electrical machines are the largest consumer of electricity worldwide, their fault diagnostic at the incipient stage and condition monitoring is essential for better reliability, economy, and safety of operation. Out of several condition monitoring techniques, motor current signature analysis is gaining heightened popularity because of its non‐invasive nature, the least number of sensors required and versatility of compatible algorithms. In this study, the best characteristics of infinite impulse response (IIR) filter are exploited to observe the broken rotor bar (BRB) frequencies with good legibility in current and voltage spectrum of the grid and inverter‐fed motor, respectively. The causes of various harmonics in the stator current spectrum are first investigated for better understanding. The results are taken based on simulation and measurements taken from the laboratory setup. It is observed that a better tuning of IIR filters can make diagnostic algorithms capable of detecting the frequencies of interest by effectively attenuating the fundamental component and reducing its spectral leakage. Moreover, in case of direct torque control‐based industrial inverter‐fed motors, the current cannot be a good candidate for fault diagnostics rather the phase voltage can be effectively used for the detection of BRBs.