Low-power BLDC motors are often and willingly used in many drive devices due to their functional advantages. They are also used in advanced positioning systems, where their good dynamic performance parameters are used. The control systems use shaft position sensors mounted on motors, the structure of which is based on magnetic elements and Hall sensors. The aim of this article was to investigate the influence of the BLDC motor quality on the correct operation of the control semiconductor system. The article presents the effect of BLDC motor shaft observation system’s inaccuracies on the friction and current amplitudes of individual inverter keys. Waveforms of the controller phase currents are considered and recorded on a test bench that allows precise sensor position changes. In addition, the effect of sensor misalignment on power losses in individual inverter transistors is investigated. The article shows a significant influence of the motor shaft observation system’s assembly accuracy on the current amplitudes of individual driver transistors and their power losses, which makes it necessary to consider these parameters when constructing power electronic systems.

This article presents an estimation method of the BLDC rotor position with asymmetrically arranged Hall sensors. Position estimation is necessary to control the motor by methods other than block commutation. A sinusoidal control method was selected for the research, which significantly reduces torque ripples and acoustic noise and is quite simple to implement. Inaccurate performance of the elements determining the position of the BLDC motor rotor causes a large error in the position estimation and has a negative impact on the operation of the drive controlled in this way. Using the developed control algorithms, it is possible to correctly determine the mechanical position of the rotor even for multi-pole motors. The proposed method is relatively easy to implement and does not require modification of control systems, being limited to changes only in the software of such devices. The tests of the actual system clearly show the usefulness of such a control method and its effectiveness.

This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, motor current signature analysis, fishbone diagrams, fault trees, multi-agent systems, image recognition, and machine learning techniques. However, there is a noticeable gap in comprehensive studies that specifically address classification of the multiple types of system components failures, class imbalance in the dataset, and the need to reduce data transmitted over the elevator’s internal bus. The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. This was achieved by constructing a test bench with a prototype cabin door drive system, identifying the most frequent system faults, developing a data preprocessing method that aggregates every driving cycle to one sample, reducing the transmitted data by 300 times, and using machine learning for modeling. A comparative analysis of the fault detection performance of seven different machine learning algorithms was conducted. An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. Finally, the developed data preparation method was implemented in the cabin door drive controller.

This article investigates the influence of faults in the phase current measurement channel on torque generation in permanent magnet synchronous motor (PMSM) drives. It is demonstrated that measurement errors, depending on their type and origin, significantly distort the actual electromagnetic torque as a function of the motor shaft angle. Since the current controller operates on incorrect values, indirect diagnostic methods are required. This study shows that analyzing the output signals of the current component controllers, particularly the torque regulator output and its phase relation to the electrical or mechanical angle, enables fault detection and classification. The waveform, frequency, and amplitude of these signals provide information about the fault’s nature, with offset errors having a more pronounced impact on torque across the entire load range than gain-related errors.

The efficiency of vibration-based energy harvesters is often constrained by low vibration amplitudes and limited responsiveness beyond the resonant frequency. To overcome these limitations, mechanical amplifiers and spring bumpers can be employed to enhance excitation amplitude and magnet...

Electromechanical energy harvesters are used to extract energy from vibrations occurring in nature, transport, or industry. The main problem with such solutions is that their output voltage is completely dependent on the frequency and amplitude of the vibrations, which can make it difficult to power a specific device or charge a battery. Therefore, it is necessary to use solutions that meet these requirements. Most harvesters contain additional, specialized mechanical gearboxes, called mechanical rectifiers or power electronic interfaces, used to match the harvester’s output voltage to the load. Design work was carried out, the construction of the proposed energy harvester was described, and the operation principle of the author’s control algorithm was presented. The results of the research confirm the possibilities of influencing the output voltage and power of the harvester system independently of the frequency and excitation amplitude.