Permanent magnet synchronous motors are the dominant technology in industrial applications such as elevator systems. Their unique advantages over induction motors give them higher energy efficiency and significant reduction in energy consumption. Accordingly, the elevator is one of the basic means of comfortable and safe transportation. More generally, in elevator systems, electric motors are characterized by continuous use, increasing the risk of possible failure that may affect the operation of the system and the safety of passengers. The application of appropriate monitoring and artificial intelligence techniques contributes to the predictive maintenance of the motor and drive system. The main objective of this paper is a literature review on the application of modern monitoring methodologies using smart sensors and machine learning algorithms for early fault diagnosis and predictive maintenance generally. Thus, by exploiting the advantages and disadvantages of each method, a technique based on a multi-fault set is developed that can be integrated into an elevator control system offering desired results of immediate predictive maintenance.

Elevators are a vital part of urban infrastructure, playing a key role in smart cities where increasing population density has driven the rise in taller buildings. As an essential means of vertical transportation, elevators have become an integral part of daily life, making their design, construction, and maintenance crucial to ensuring safety and compliance with evolving industry standards. The safety of elevator systems depends on the continuous monitoring and fault-free operation of Permanent Magnet Synchronous Motor (PMSM) drives, which are critical to their performance. Furthermore, the fault-free operation of PMSM drives reduces operating costs, increases service life, and improves reliability. The PMSM drive components may be susceptible to electrical, mechanical, and thermal faults that, if undetected, can lead to operational disruptions or safety risks. The integration of artificial intelligence and Internet of Things (IoT) technologies can enhance fault prediction, reducing downtime and improving efficiency. Ongoing challenges such as managing machine thermal load and developing more durable materials for PMSMs require the development of suitable models that are adapted to existing drive systems. The proposed framework for fault prediction is validated on a real residential elevator equipped with a PMSM drive. Multimodal signal data is processed through a Generative Adversarial Network (GAN)-enhanced Positive Unlabeled (PU) classifier and a Reinforcement Learning (RL)-based adaptive decision engine, enabling robust and scalable fault prediction in a non-intrusive fashion.

Induction motors (IMs) are crucial in many industrial applications, offering a cost-effective and reliable source of power transmission and generation. However, their continuous operation imposes considerable stress on electrical and mechanical parts, leading to progressive wear that can cause unexpected system shutdowns. Bearings, which enable shaft motion and reduce friction under varying loads, are the most failure-prone components, with bearing ball defects representing most severe mechanical failures. Early and accurate fault diagnosis is therefore essential to prevent damage and ensure operational continuity. Recent advances in the Internet of Things (IoT) and machine learning (ML) have enabled timely and effective predictive maintenance strategies. Among various diagnostic parameters, vibration analysis has proven particularly effective for detecting bearing faults. This study proposes a hybrid diagnostic framework for induction motor bearings, combining vibration signal analysis with Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) in an IoT-enabled Industry 4.0 architecture. Statistical and frequency-domain features were extracted, reduced using Principal Component Analysis (PCA), and classified with SVMs and ANNs, achieving over 95% accuracy. The novelty of this work lies in the hybrid integration of interpretable and non-linear ML models within an IoT-based edge–cloud framework. Its main contribution is a scalable and accurate real-time predictive maintenance solution, ensuring high diagnostic reliability and seamless integration in Industry 4.0 environments.

The applications of the permanent magnet synchronous motor (PMSM) are the most seen in the elevator industry due to their high efficiency, low losses and the potential for high energy savings. The Internet of Things (IoT) is a modern technology which is being incorporated in various industrial applications, especially in electrical machines as a means of control, monitoring and preventive maintenance. This paper is focused on reviewing the use PMSM in lift systems, the application of various condition monitoring techniques and real-time data collection techniques using IoT technology. In addition, we focus on different categories of industrial sensors, their connectivity and the standards they should meet for PMSMs used in elevator applications. Finally, we analyze various secure ways of transmitting data on different platforms so that the transmission of information takes into account possible unwanted instructions from exogenous factors.

This paper presents the energy savings achieved by using a particular three-phase permanent-magnet motor drive control strategy in an elevator application. The proposed control methodology, based on a particular variable-amplitude variable-frequency voltage control pattern technique implemented in a permanent-magnet motor, is compared to a standard induction motor elevator case. By adopting appropriate simultaneous changes in the amplitude and frequency of the motor voltage, high speeds can be attained in conjunction with smooth starting and stopping actions involving a reduced supply current during the respective movement of the elevator. In addition, this method exhibits a high power factor with a good driving quality. The control technique introduced achieves the levelling-off of the floor and the group movement of the system using in the programmable memory a speed pattern that is generated targeting proportionality to the position of the lift. In that respect, significant energy savings can be obtained, which, depending on the type of motor implemented, can be up to 30% compared to the conventional techniques. These improvements can be attained with the appropriate handling of the applied pulse width modulation techniques. Various simulated and experimental results are given, illustrating the respective energy savings achieved with the proposed methodology.

Elevators serve as essential vertical transportation systems for both passengers and heavy loads in modern buildings. Electromechanical lifts have become the dominant choice due to their performance advantages over hydraulic systems. A critical component of their drive mechanism is the Permanent Magnet Synchronous Motor (PMSM), which is subject to mechanical and electrical stress during continuous operation. This necessitates advanced monitoring techniques to ensure safety, system reliability, and reduced maintenance costs. In this study, a fault-tolerant PMSM is designed and evaluated through 2D Finite Element Analysis (FEA), optimizing key electromagnetic parameters. The design is validated through experimental testing on a real elevator setup, capturing operational data under various loading conditions. These signals are preprocessed and analyzed using advanced machine-learning techniques, specifically a Random Forest classifier, to distinguish between Normal, Marginal, and Critical states of motor health. The model achieved a classification accuracy of 94%, demonstrating high precision in predictive maintenance capabilities. The results confirm that integrating a fault-tolerant PMSM design with real-time data analytics offers a reliable solution for early fault detection, minimizing downtime and enhancing elevator safety.