Biblio

In this work, a novel approach to detect and diagnose single and combined faults in the Induction Motor (IM) is proposed. In Condition Monitoring Systems (CMS) based on the Motor Current Signature Analysis (MCSA), the simultaneous occurrence of multiple faults is a major challenge. An innovative technique called Multiple Windowed Harmonic Wavelet Packet Transform (MWHWPT) is used in order to discriminate between the faulty components of the IM, even during compound faults. Thus, each motor component is monitored by a specific Fault Index (FI) which allows the fault diagnosis without the need for a classifier. The tests carried on Rotor and Bearing faults show high fault diagnosis rate even during compound faults and proves the competitive performance of the proposed approach with literature works.

This paper presents a method optimizing the thrust force of a Micro Electro Mechanical System (MEMS) Permanent Magnet Linear Motor, based on harmonic current injection. Fourier decomposition is implemented to the air gap flux density of the motor to derive the fitting expression of the thrust force dependent to exciting current. Through analyzing the thrust force ripple of sinusoidal current excitement, the paper comes up with the strategy of harmonic current injection to eliminate the ripple component in the thrust force waveform. Mathematical demonstration is given that injecting harmonic current can totally eliminate the ripple caused by odd component of vertical air gap magnetic induction intensity. Simulation verification is implemented based on the 3rd and 7th harmonic injection control strategy, proving that the method is feasible for the thrust ripple is reduced to 4.3% of the value before optimazation. Experimental results lead to the consistent conclusion that the strategy shows good steady-state and dynamic performance.

The determination of the damper cage health is a matter of great importance in those industries that use large synchronous motors in their processes. In the past, unexpected damages of that element implied economic losses amounting up to several million \$. The problem is that, in the technical literature, there is a lack of non-invasive techniques enabling the reliable condition monitoring of this element. This explains the fact that, in industry, rudimentary methods are still employed to determine its condition. This paper proposes the analysis of the stray flux as a way to determine the condition of the damper cage. The paper shows that the analysis of the stray flux under starting yields characteristic time-frequency signatures of the fault components that can be used to reliably determine the condition of the damper. Moreover, the analysis of the stray flux at steady-state operation under asynchronous mode could give useful information to this end. The paper also analyses the influence of the remanent magnetism in the rotor of some synchronous motors, which can make the damper cage diagnosis more difficult; some solutions to this problem are also suggested in the paper.

Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is proposed to detect common faults in the electric powertrains. The proposed method is based on pattern recognition using convolutional neural network to detect effectively not only single faults at constant speed but also multiple faults in variable speed operations. The effectiveness of the proposed method is validated via an in-house experimental setup.

Conducted emission of motors is a domain of interest for EMC as it may introduce disturbances in the system in which they are integrated. Nevertheless few publications deal with the susceptibility of motors, and especially, servomotors despite this devices are more and more used in automated production lines as well as for robotics. Recent papers have been released devoted to the possibility of compromising such systems by cyber-attacks. One could imagine the use of smart intentional electromagnetic interference to modify their behavior or damage them leading in the modification of the industrial process. This paper aims to identify the disturbances that may affect the behavior of a Commercial Off-The-Shelf servomotor when exposed to an electromagnetic field and the criticality of the effects with regards to its application. Experiments have shown that a train of radio frequency pulses may induce an erroneous reading of the position value of the servomotor and modify in an unpredictable way the movement of the motor's axis.