Degradation feature extraction method for piezoelectric ceramic of ultrasonic motor based on DCT-SV cross entropy

Crack on piezoelectric ceramic is the main reason leading to failure of ultrasonic motors. A novel degradation feature extraction method based on discrete cosine transform (DCT) -singular value (SV) cross entropy was proposed in this paper. In order to improve the correlation with the crack, the DCT coefficients with the property of energy aggregation, were used to extract fault information. To avoid the influence of human factors in traditional DCT de-noising method, a matrix composed of DCT coefficients was constructed, and the SV cross entropy of the matrix was taken as the degradation feature for ultrasonic motor. A numerical simulated noise was added to the measured signal to verify the anti-noise performance of the feature. Analysis of the experimental results demonstrates that the proposed DCT-SV cross entropy is feasible and effective in indicating the degradation of piezoelectric ceramic in ultrasonic motor

Bearing Fault Detection by One-Dimensional Convolutional Neural Networks

Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy

Condition monitoring of induction motors in the nuclear power station environment

The induction motor is a highly utilised electrical machine in industry, with the nuclear industry being no exception. A typical nuclear power station usually contains more than 1000 motors, where they are used in safety and non-safety application. The efficient and fault-free operation of this machine is critical to the safe and economical operation of any plant, including nuclear power stations. A comprehensive literature review was conducted that covered the functioning of the induction machine, its common faults and methods of detecting these faults. The Condition Based Maintenance framework was introduced in which condition monitoring of induction machines is an essential component. The main condition monitoring methods were explained with the main focus being on Motor Current Signature Analysis (MCSA) and the various methods associated with it. Three analysis methods were selected for further study, namely, Current Signature Analysis, Instantaneous Power Signature Analysis (IPSA) and Motor Square Current Signature Analysis (MSCSA). Essentially, the methodology used in this dissertation was to study the three common motor faults (bearings, stator and rotor cage) in isolation and compare the results to that of the healthy motor of the same type. The test loads as well as fault severity were varied where possible to investigate its effect on the fault detection scheme. The data was processed using an FFT based algorithm programed in MATLAB. The results of the study of the three spectral analysis techniques showed that no single technique is able to detect motor faults under all tested circumstances. The MCSA technique proved the most capable of the three techniques as it was able to detect faults under most conditions, but generally suffered poor results in inverter driven motor applications. The IPSA and MSCSA techniques performed selectively when compared to MCSA and were relatively successful when detecting the mechanical faults. The fact that the former techniques produce results at unique points in the spectrum would suggest that they are more suitable for verifying results. As part of a comprehensive condition monitoring scheme, as required by a large population of the motors on a nuclear power station, the three techniques presented in this study could readily be incorporated into the Condition Based Maintenance framework where the strengths of each could be exploited

Online condition monitoring and fault detection in induction motor bearings

Induction motors (IMs) are commonly used in industry. Online IM health condition monitoring aims to recognize motor defect at its early stage to prevent motor performance degradation and reduce maintenance costs. The most common fault in IMs is related to bearing defects. Although many signal processing techniques have been proposed in literature for bearing fault detection using vibration and stator current signals, reliable bearing fault diagnosis still remains a challenging task. One of the reasons is that a rolling element bearing is not a simple component, but a system; its related features could be time-varying and nonlinear in nature. The objective of this study is to investigate an online condition monitoring system for IM bearing fault detection. The monitoring system consists of two main modules: smart data acquisition (DAQ) and bearing fault detection. In this work, a smart current sensor system is developed for data acquisition wirelessly. The DAQ system is tested for wireless data transmission, consistent data sampling, and low power consumption. The data acquisition operation is controlled by using an adaptive interface. In bearing fault detection, a generalized Teager-Kaiser energy (GTKE) technique is proposed for nonlinear bearing feature extraction and fault detection using both vibration and current signals. The proposed GTKE technique will demodulate the signal by tracking the instantaneous signal energy. An optimization method is proposed to enhance the fault-related features and improve signal-to-noise ratio. The effectiveness of the proposed technique is verified experimentally using a series of IM tests. The robustness is examined under different operating conditions

General Anesthesia as a Multimodal Individualized Clinical Concept

In this book, a series of modern multimodal monitoring techniques during general anesthesia are presented, with a focus on patient-oriented anesthesia based on the individual needs of each patient reflected in the degree of hypnosis, the nociception–antinociception balance, and neuromuscular transmission. Moreover, a series of secondary implications for hemodynamic status, post-anesthetic recovery, and patient satisfaction are highlighted