This paper introduces a method to detect a fault associated with critical components/subsystems of an engineered system. It is required, in this case, to detect the fault condition as early as possible, with specified degree of confidence and a prescribed false alarm rate. Innovative features of the enabling technologies include a Bayesian estimation algorithm called particle filtering, which employs features or condition indicators derived from sensor data in combination with simple models of the system's degrading state to detect a deviation or discrepancy between a baseline (no-fault) distribution and its current counterpart. The scheme requires a fault progression model describing the degrading state of the system in the operation. A generic model based on fatigue analysis is provided and its parameters adaptation is discussed in detail. The scheme provides the probability of abnormal condition and the presence of a fault is confirmed for a given confidence level. The efficacy of the proposed approach is illustrated with data acquired from bearings typically found on aircraft and monitored via a properly instrumented test rig.

This paper presents a simple and efficient linear phase lead compensation repetitive control scheme for engineers to develop high-performance power converter systems. The linear phase lead compensation helps a repetitive controller to achieve faster convergence rate, higher tracking accuracy, and wider stability region. In the proposed scheme, the phase lead compensation repetitive controller is plugged into generic state-feedback-controlled converter systems. Its comprehensive synthesis, which involves principle, analysis, design, modeling, implementation, and experiments, is systematically and completely presented in great detail. A complete series of experiments is successfully carried out to verify the solution.

Machine prognosis is a significant part of condition-based maintenance and intends to monitor and track the time evolution of a fault so that maintenance can be performed or the task can be terminated to avoid a catastrophic failure. A new prognostic method is developed in this paper using adaptive neuro-fuzzy inference systems (ANFISs) and high-order particle filtering. The ANFIS is trained via machine historical failure data. The trained ANFIS and its modeling noise constitute an m th-order hidden Markov model to describe the fault propagation process. The high-order particle filter uses this Markov model to predict the time evolution of the fault indicator in the form of a probability density function. An online update scheme is developed to adapt the Markov model to various machine dynamics quickly. The performance of the proposed method is evaluated by using the testing data from a cracked carrier plate and a faulty bearing. Results show that it outperforms classical condition predictors.

Accurate fault information is critical for optimal scheduling of production activities, improving system reliability, and reducing operation and maintenance cost. In recent years, many fault diagnosis methods for rolling element bearings have been developed based on deep learning. Most of them are totally data-driven and do not consider the domain knowledge that has been used in fault diagnosis for years. Meanwhile, operating conditions such as rotating speed and load that have great influence on vibration signals are also ignored. It may cause a decrease in accuracy when bearing type or operating condition changes. To address these problems, this paper proposes a rolling element bearing fault diagnosis and localization approach based on multi-task convolutional neural network (CNN) with information fusion. In the proposed approach, domain knowledge, operating conditions, and vibration signals are fused into a 3-dimensional input that can be processed well by CNN. Then a multi-task CNN with dynamic training rates is constructed to simultaneously accomplish two tasks, fault diagnosis and localization. Experimental results on two rolling element bearing testbeds with different bearing types and operating conditions are presented and compared with existing state-of-the-art approaches to demonstrate the effectiveness and accuracy of the proposed approach.

The estimation and prediction of State-of-Health (SOH) and State-of-Charge (SOC) of Lithium-ion batteries are two main functions of battery management system (BMS). In order to reduce the computation cost and enable deployment of BMS on low-cost hardware, Lebesgue sampling based extended Kalman filter (LS-EKF) is developed to estimate SOH and SOC. LS-EKF is able to eliminate unnecessary computations, especially when the states change slowly. In this paper, the SOH is firstly estimated and the remaining useful life (RUL) is predicted by LS-EKF. Then, the estimated SOH is used as the initial battery capacity for SOC estimation and prediction. The SOH and SOC estimation and prediction are calculated repeatedly in the whole battery service life. The proposed method is verified with the application to the capacity degradation of Lithium-ion battery. The results show that the LS-EKF based algorithm has a good performance in SOH and SOC estimation and prediction in terms of accuracy and computation cost.

In this paper, a zero-phase odd-harmonic repetitive control scheme is proposed for pulse-width modulation inverters. The proposed repetitive controller combines an odd-harmonic periodic generator with a noncasual zero-phase compensation filter. It occupies less data memory than a conventional repetitive controller does. Moreover, it offers faster convergence of the tracking error, and yields very low total harmonics distortion (THD) and low tracking error. Analysis and design of the proposed system are presented. Experimental results with the proposed repetitive controller are presented to validate the approach. The phenomena of even-harmonic residues in the proposed control system is discussed and experimentally demonstrated.