Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

Application of the fuzzy min-max neural network to fault detection and diagnosis of induction motors

In this paper, an application of the motor current signature analysis (MCSA) method and the fuzzy min–max (FMM) neural network to detection and classification of induction motor faults is described. The finite element method is employed to generate simulated data pertaining to changes in the stator current signatures under different motor conditions. The MCSA method is then used to process the stator current signatures. Specifically, the power spectral density is employed to extract harmonics features for fault detection and classification with the FMM network. Various types of induction motor faults, which include stator winding faults and eccentricity problems, under different load conditions are experimented. The results are analyzed and compared with those from other methods. The outcomes indicate that the proposed technique is effective for fault detection and diagnosis of induction motors under different conditions.

Modern enhancements in teaching design theories to mechanical engineering students

This paper describes the application of computer aided design (CAD) in teaching advanced design methodologies to fourth-year undergraduate students majoring in mechanical engineering. This involves modern enhancements in teaching strategies for subjects such as design-for-X (DFx) and failure mode effect analysis (FMEA) concepts, which are traditionally categorised as advanced design methodologies. The main subsets of DFx including design-for-assembly (DFA), design-for-disassembly (DFD), design-for-manufacturing (DFM), design-for-environment (DFE) and design-for-recyclability (DFR) were covered by studying various engineering and consumer products. The unit was designed as a combination of practical hands-on workshop-based classes along with a software-based evaluation of different products. In addition to CAD, finite element modelling techniques were utilised to enhance the students’ understanding of design faults and failures. The inquiry into teaching practice and design of this fourth-year unit was carried out during past two years and it revealed some interesting outcomes from our teaching practice in terms of students’ learning experiences. Finally, the paper discusses some critical factors in the context of teaching advanced design methodologies to the undergraduates in mechanical engineering and even manufacturing engineering.

A fault detection technique for dynamical systems

This paper shows how a functional observer can be utilized to detect faults in LTI MIMO systems. The fault detection technique is designed so that the functional observer based fault indicator asymptotically converges to a fault indicator that can be derived based on the nominal system. The asymptotic value of the proposed fault indicator is not dependent on the functional observer parameters; moreover, by choosing appropriate functional observer parameters the convergence rate of the fault indicator can be altered. Observability of the system is not a requirement for the design of the fault detection scheme.

Fault detection for dynamical systems using first-order functional observers

This paper reports a new result on the fault detection of dynamical systems by employing only first-order functional observers. Indeed, we show that fault detection can be achieved by utilizing first-order functional observers. The advantages for having such simple structured observers are obvious from the economical and practical points of view as significant cost saving can be achieved. We derive existence conditions and an algorithm for the generation of residual signals to detect faults using firstorder functional observers. Two numerical examples are given to illustrate the proposed fault detection scheme. In one of the examples, a two-area interconnected power system with reheat thermal turbines is considered where only a first-order functional observer is designed to detect faults in the power system.

Online motor fault detection and diagnosis using a hybrid FMM-CART model

In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.

Mohair fibre quality and production as related to animal size

Mohair is a luxury fibre produced by Angora goats. Mohair has special textile properties and is famed for its lustre. There are many reports of the relationships between mohair attributes and processing. While mohair production can be profitable to farmers there are severe price discounts for faults and poor quality. While genetics is known to affect mohair quality, fundamental relationships between body size and mohair quality have not been determined. We conducted a series of investigations to quantify the relationships between important mohair quality attributes and live weight and other lifetime factors associated with Angora goats.

Improving fault ride through capability of DFIG during RSC flashover fault

Wind power generation is growing rapidly. However, maintaining the wind turbine connection to grid is a real challenge. Recent grid codes require wind turbines to maintain connected to the grid even during fault conditions which increases concerns about its sensitivity to external faults. So, researchers have given attention to investigating the impact of various external faults, and grid disturbances such as voltage sag and short circuit faults, on the fault ride through (FRT) capability of the doubly fed induction generator (DFIG). However, no attention has been given to the impact of internal faults on the dynamic performance of the machine when the fault occurs within the voltage source converters (VSCs) that interface the DFIG with the grid. This paper investigates the impact of the rotor side converter (RSC) IGBT flashover fault on the common coupling (PCC) reactive power and the FRT is proposed. The DFIG compliance with numerous and recently released FRT grid codes under the studied fault, with and without the STATCOM are examined and compared. Furthermore, the capability of a proposed controller to bring the voltage profile at the point of PCC to the nominal steady-state level; maintain the unity power factor operation; and, maintain the connection of the wind turbine to the grid are examined

Impact of nanodiffusion on the stacking fault energy in high-strength steels

A key requirement of modern steels – the combination of high strength and high deformability – can best be achieved by enabling a local adaptation of the microstructure during deformation. A local hardening is most efficiently obtained by a modification of the stacking sequence of atomic layers, resulting in the formation of twins or martensite. Combining ab initio calculations with in situ transmission electron microscopy, we show that the ability of a material to incorporate such stacking faults depends on its overall chemical composition and, importantly, the local composition near the defect, which is controlled by nanodiffusion. Specifically, the role of carbon for the stacking fault energy in high-Mn steels is investigated. Consequences for the long-term mechanical properties and the characterisation of these materials are discussed.

Approach to fault detection of time-delay systems using functional observers

A new methodology is reported for designing functional observers to detect actuator faults of a class of time-delay systems where the matrix pair (A, C) is not observable. First, a generalised state transformation is used to transform the system into new coordinates where the delay term associated with the state vector is injected into the system's output and input. Then, a minimum-order functional observer is designed to construct a residual function that can trigger system faults. The finding is significant as it is now possible to detect faults of time-delay systems where the pair (A, C) is not required to be observable. A numerical example is given to illustrate the effectiveness of the proposed design approach. © The Institution of Engineering and Technology 2014.

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.

Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

In this paper, a hybrid online learning model that combines the fuzzy min-max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks. © 2014 Springer Science+Business Media New York.

Severe plastic deformation of TWIP steel

The severe plastic deformation of a Twinning Induced Plasticity (TWIP), 0.61C-22.3Mn-0.19Si-0.14Ni-0.27Cr (wt. %) steel by Equal Channel Angular Pressing (ECAP) at elevated temperatures was used to study the deformation mechanism as a function of accumulated strain and processing parameters. The relationship between the microstructures after different deformation schedules of ECAP at the temperatures of 200, 300 and 400oC, strain hardening behavior and mechanical properties was studied. The best balance between strength and ductility (1702 MPa and 24%) was found after 2 passes at 400oC and 300oC of ECAP. It was due to the formation of deformation microbands and twins in the microstructure. The twinning was observed after all deformation schedules except after 1 pass at 400oC. The important finding was the formation of twins in the ultrafine grains. Moreover, the stacking faults were observed in the subgrains with the size of 50nm. It is also worth mentioning the formation of nano- twins within the micro-twins at the same time. It was found that the deformation schedule affects the dislocation substructure with formation of deformation bands, cells, subgrains, two variants of twins that, in turn, influence the strain-hardening behavior and mechanical properties. Keywords: Twinning Induced Plasticity steels; Equal Channel Angular Pressing; mechanical properties; transmission electron microscopy; micro/nano twins; dislocation substructure.

A multi-agent approach for enhancing transient stability of smart grids

Transient stability, an important issue to avoid the loss of synchronous operation in power systems, can be achieved through proper coordination and operation of protective devices within the critical clearing time (CCT). In view of this, the development of an intelligent decision support system is useful for providing better protection relay coordination. This paper presents an intelligent distributed agent-based scheme to enhance the transient stability of smart grids in light of CCT where a multi-agent framework (MAF) is developed and the agents are represented in such a way that they are equipped with protection relays (PRs). In addition to this, an algorithm is developed which assists the agents to make autonomous decision for controlling circuit breakers (CBs) independently. The proposed agents are responsible for the coordination of protection devices which is done through the precise detection and isolation of faults within the CCT. The agents also perform the duty of reclosing CBs after the clearance of faults. The performance of the proposed approach is demonstrated on a standard IEEE 39-bus test system by considering short-circuit faults at different locations under various load conditions. To further validate the suitability of the proposed scheme a benchmark 16-machine 68-bus power system is also considered. Simulation results show that MAF exhibits full flexibility to adapt the changes in system configurations and increase the stability margin for both test systems.