A survey on networked control systems (NCSs), published in a previous paper, is updated and extended. A simple framework and some general formulations for the study of NCSs are proposed. In addition to the survey on NCSs, the impact of NCSs on traditional large-scale system control methodologies with a related application is also reviewed.

Networked control system: a brief survey

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Hydraulic Control Systems

This paper presents a test benchmark model for the evaluation of fault detection and accommodation schemes. This benchmark model deals with the wind turbine on a system level, and it includes sensor, actuator, and system faults, namely faults in the pitch system, the drive train, the generator, and the converter system. Since it is a system-level model, converter and pitch system models are simplified because these are controlled by internal controllers working at higher frequencies than the system model. The model represents a three-bladed pitch-controlled variable-speed wind turbine with a nominal power of 4.8 MW. The fault detection and isolation (FDI) problem was addressed by several teams, and five of the solutions are compared in the second part of this paper. This comparison relies on additional test data in which the faults occur in different operating conditions than in the test data used for the FDI design.

/pdf/fault-tolerant-control-of-wind-turbines-a-benchmark-model-3rp8uv86oa.pdf

Fault-Tolerant Control of Wind Turbines: A Benchmark Model

Bearing faults are the main contributors to the failure of electric motors. Although a number of vibration analysis methods have been developed for the detection of bearing faults, false alarms still result in losses. This paper presents a method that detects bearing faults and monitors the degradation of bearings in electric motors. Based on spectral kurtosis (SK) and cross correlation, the method extracts fault features that represent different faults, and the features are then combined to form a health index using principal component analysis (PCA) and a semisupervised k -nearest neighbor (KNN) distance measure. The method was validated by experiments using a machinery fault simulator and a computer cooling fan motor bearing. The method is able to detect incipient faults and diagnose the locations of faults under masking noise. It also provides a health index that tracks the degradation of faults without missing intermittent faults. Moreover, faulty reference data are not required.

Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K -Nearest Neighbor Distance Analysis

This paper investigates the problem of automatic speed tracking control of an electric vehicle (EV) that is powered by a permanent-magnet synchronous motor (PMSM). A reconfiguration scheme, based on higher order sliding mode (HOSM) observer, is proposed in the event of sensor faults/failures to maintain a good control performance. The corresponding controlled motor output torque drives EVs to track the desired vehicle reference speed for providing uninterrupted vehicle safe operation. The effectiveness of the overall sensor fault-tolerant speed tracking control is highlighted when an EV is subjected to disturbances like aerodynamic load force and road roughness using high-fidelity software package CarSim. Experiments with a 26-W, three-phase PMSM are presented to demonstrate the validity of the proposed fault-detection scheme.

A Robust Observer-Based Sensor Fault-Tolerant Control for PMSM in Electric Vehicles

This paper addresses the application of rotor speed signal for the detection and diagnosis of ball bearing faults in rotating electrical machines. Many existing techniques for bearing fault diagnosis (BFD) rely on vibration signals or current signals. However, vibration- or current-based BFD techniques suffer from various challenges that must be addressed. As an alternative, this paper takes the initial step of investigating the efficiency of rotor speed monitoring for BFD. The bearing failure modes are reviewed and their effects on the rotor speed signal are described. Based on this analysis, a novel BFD technique, the rotor speed-based BFD (RSB-BFD) method under variable speed and constant load conditions, is proposed to provide a benefit in terms of cost and simplicity. The proposed RSB-BFD method exploits the absolute value-based principal component analysis (PCA), which improves the performance of classical PCA by using the absolute value of weights and the sum square error. The performance and effectiveness of the RSB-BFD method is demonstrated using an experimental setup with a set of realistic bearing faults in the outer race, inner race, and balls.

/pdf/rotor-speed-based-bearing-fault-diagnosis-rsb-bfd-under-585dxx0uz8.pdf

Rotor Speed-Based Bearing Fault Diagnosis (RSB-BFD) Under Variable Speed and Constant Load

This paper addresses the development of an algorithm that can improve the signal-to-noise ratio (SNR) in inchoate faulty signals. The removal of noise and preservation of fault information components cannot be easily achieved. Many techniques for SNR improvement in healthy signals rely on frequency bands. Such techniques have been proven to be efficient in improving the SRN by filtering out frequency bands (FoFBs). However, these techniques cannot reduce noise and preserve fault information when dealing with inchoate faulty signals. Thus, a feature extraction technique based on statistical parameters, which are free from Gaussian noise, is proposed in this paper. The proposed signal subspace-based approach for SNR improvement in inchoate faulty signals is based on a modified principal component analysis (PCA), in which the optimal subspace is selected via a cumulative percent of variance (CPV) criterion and the test statistic condition of the true information loss, which has the tendency to alleviate the impact of Gaussian and non-Gaussian noise and provides useful time domain analysis for non-stationary signals such as vibration, in which spectral contents vary with respect to time. Furthermore, the modified PCA algorithm is combined with a low-pass filter (LPF) to achieve an optimum balance between noise reduction efficiency and the conservation of inchoate fault information. The proposed PCA-LPF algorithm is compared with different filters under different noise levels to find the most efficient approach in terms of optimizing the trade-off between noise reduction efficiency and precision of inchoate fault information conservation, with the final goal of improving the fault detection capability. Further, the performance of the proposed PCA-LPF algorithm was demonstrated with an experimental study on vibration-based ball bearing fault detection.

Dongik Lee

Papers

Rotor Speed-Based Bearing Fault Diagnosis (RSB-BFD) Under Variable Speed and Constant Load

Principal component analysis based signal-to-noise ratio improvement for inchoate faulty signals: Application to ball bearing fault detection

Sliding mode high-gain observers for a class of uncertain nonlinear systems

PID control for a distributed system with a smart actuator

A CANbus-based safety-critical distributed aeroengine control systems architecture demonstrator