Robust functional estimation and fault diagnosis for complex uncertain systems

System monitoring and fault diagnosis capabilities are the most important aspects in improving safety and reliability of automatic control systems. This research proposed new methodologies on fault diagnosis and estimation for complex uncertain systems. As a result of this research, complex industrial plants can now be more effectively controlled.

Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.

A brief review on offshore wind turbine fault detection and recent development in condition monitoring based maintenance system

Offshore wind turbine requires more systematized operation and maintenance strategies to ensure systems are harmless, profitable and cost-effective. Condition monitoring and fault diagnostic systems ominously plays an important role in offshore wind turbine in order to cut down maintenance and operational costs. Condition monitoring techniques which describing complex faults and failure mode types and their generated traceable signs to provide cost-effective condition monitoring and predictive maintenance and their diagnostic schemes. Continuously monitor the condition of critical parts are the most efficient way to improve reliability of wind turbine. Implementation of Condition Based Maintenance (CBM) strategy provides right time maintenance decisions and Predictive Health Monitoring (PHM) data to overcome breakdown and machine downtime. Fault detection and CBM implementation is challenging for off shore wind farm due to the complexity of remote sensing, components health and predictive assessment, data collection, data analysis, data handling, state recognition, and advisory decision. The rapid expansion of wind farms, advanced technological development and harsh installation sites needs a successful CM approach. This paper aims to review brief status of recent development of CM techniques and focusing with major faults takes place in gear box and bearing, rotor and blade, pitch, yaw and tower system and generator and control system.

Condition monitoring of induction motors : A review and an application of an ensemble of hybrid intelligent models

In this paper, a review on condition monitoring of induction motors is first presented. Then, an ensemble of hybrid intelligent models that is useful for condition monitoring of induction motors is proposed. The review covers two parts, i.e.; (i) a total of nine commonly used condition monitoring methods of induction motors; and (ii) intelligent learning models for condition monitoring of induction motors subject to single and multiple input signals. Based on the review findings, the Motor Current Signature Analysis (MCSA) method is selected for this study owing to its online, non-invasive properties and its requirement of only single input source; therefore leading to a cost-effective condition monitoring method. A hybrid intelligent model that consists of the Fuzzy Min-Max (FMM) neural network and the Random Forest (RF) model comprising an ensemble of Classification and Regression Trees is developed. The majority voting scheme is used to combine the predictions produced by the resulting FMM-RF ensemble (or FMM-RFE) members. A benchmark problem is first deployed to evaluate the usefulness of the FMM-RFE model. Then, the model is applied to condition monitoring of induction motors using a set of real data samples. Specifically, the stator current signals of induction motors are obtained using the MCSA method. The signals are processed to produce a set of harmonic-based features for classification using the FMM-RFE model. The experimental results show good performances in both noise-free and noisy environments. More importantly, a set of explanatory rules in the form of a decision tree can be extracted from the FMM-RFE model to justify its predictions. The outcomes ascertain the effectiveness of the proposed FMM-RFE model in undertaking condition monitoring tasks, especially for induction motors, under different environments. © 2014 Elsevier Ltd. All rights reserved.

Condition monitoring of broken rotor bars using a hybrid FMM-GA model

A condition monitoring system for induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and Genetic Algorithm (GA) is presented in this paper. Two types of experiments, one from the finite element method and another from real laboratory tests of broken rotor bars in an induction motor are conducted. The induction motor with broken rotor bars is operated under different load conditions. FMM is first used for learning and distinguishing between a healthy motor and one with broken rotor bars. The GA is then utilized for extracting fuzzy if-then rules using the don’t care approach in minimizing the number of rules. The results clearly demonstrate the effectiveness of the hybrid FMM-GA model in condition monitoring of broken rotor bars in induction motors.

Functional observer design with application to pre-compensated multi-variable systems

Partial state estimation of dynamical systems provides significant advantages in practical applications. Likewise, pre-compensator design for multi variable systems invokes considerable increase in the order of the original system. Hence, applying functional observer to pre-compensated systems can result in lower computational costs and more practicability in some applications such as fault diagnosis and output feedback control of these systems. In this note, functional observer design is investigated for pre-compensated systems. A lower order pre-compensator is designed based on a H2 norm optimization that is designed as the solution of a set of linear matrix inequalities (LMIs). Next, a minimum order functional observer is designed for the pre-compensated system. An LTI model of an irreversible chemical reactor is used to demonstrate our design algorithm, and to highlight the benefits of the proposed schemes.

Distributed multi-agent scheme to enhance cyber security of smart power grids

This paper presents a distributed multi-agent scheme for enhancing the cyber security of smart grids which integrates computational resources, physical processes, and communication capabilities. Smart grid infrastructures are vulnerable to various cyber attacks and noises whose influences are significant for reliable and secure operations. A distributed agent-based framework is developed to investigate the interactions between physical processes and cyber activities where the attacks are considered as additive sensor fault signals and noises as randomly generated disturbance signals. A model of innovative physical process-oriented counter-measure and abnormal angle-state observer is designed for detection and mitigation against integrity attacks. Furthermore, this model helps to identify if the observation errors are caused either by attacks or noises.

Distributed multi-agent scheme to enhance cyber security of smart power grids

This paper presents a distributed multi-agent scheme for enhancing the cyber security of smart grids which integrates computational resources, physical processes, and communication capabilities. Smart grid infrastructures are vulnerable to various cyber attacks and noises whose influences are significant for reliable and secure operations. A distributed agent-based framework is developed to investigate the interactions between physical processes and cyber activities where the attacks are considered as additive sensor fault signals and noises as randomly generated disturbance signals. A model of innovative physical process-oriented counter-measure and abnormal angle-state observer is designed for detection and mitigation against integrity attacks. Furthermore, this model helps to identify if the observation errors are caused either by attacks or noises.

Rule learning and extraction using a hybrid neural network : a case study on fault detection and diagnosis

A hybrid network, based on the integration of Fuzzy ARTMAP (FAM) and the Rectangular Basis Function Network (RecBFN), is proposed for rule learning and extraction problems. The underlying idea for such integration is that FAM operates as a classifier to cluster data samples based on similarity, while the RecBFN acts as a “compressor” to extract and refine knowledge learned by the trained FAM network. The hybrid network is capable of classifying data samples incrementally as well as of acquiring rules directly from data samples for explaining its predictions. To evaluate the effectiveness of the hybrid network, it is applied to a fault detection and diagnosis task by using a set of real sensor data collected from a Circulating Water (CW) system in a power generation plant. The rules extracted from the network are analyzed and discussed, and are found to be in agreement with experts’ opinions used in maintaining the CW system.

A hybrid intelligent system and its application to fault detection and diagnosis

This paper proposes a hybrid system that integrates the SOM (Self Organizing Map) neural network, the kMER (kernel-based Maximum Entropy learning Rule) algorithm and the Probabilistic Neural Network (PNN) for data visualization and classification. The rationales of this hybrid SOM-kMER-PNN model are explained, and the applicability of the proposed model is demonstrated using two benchmark data sets and a real-world application to fault detection and diagnosis. The outcomes show that the hybrid system is able to achieve comparable classification rates when compared to those from a number of existing classifiers and, at the same time, to produce meaningful visualization of the data sets.