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The aim of this paper is to introduce an accurate asymmetric fault index for the diagnosis of the faulty linear permanent magnet Vernier machine (LPMVM).

Three-dimensional finite element method is applied to model the LPMVM. The geometrical and physical properties of the machine, the effect of stator and translator teeth, magnetic saturation of core and nonuniform air gap due to asymmetric fault are taken into account in the simulation. The air gap asymmetric fault is proposed. This analytical method estimates the air gap flux density of an LPMVM.

This paper presents an analytical method to predict the performance of a healthy and faulty LPMVM. The introduced index is based on the frequency patterns of the stator current. Besides, the robustness of the index in different loads and fault severity is addressed.

Introducing index for air gap asymmetry fault diagnosis of LPMVM.

The purpose of this paper is to provide a new method of the fault diagnosis of wireless sensor networks (WSNs) node, which is based on wavelet neural network (WNN).

The approach uses WNN to diagnose the sensor module of the node.

The method based on WNN sensing parts of the WSN nodes in additional fault location is accurate feasible.

The fault of WSNs node protean, it is necessary to establish even more fault model for the training of WNN.

The simulation results provide useful guidelines for the engineers faced with the detection the fault of the WSN node.

The WNN is well‐known. The innovation here is applying this method in order to diagnose the fault of WSNs node.

The purpose of this study is to present a new integrated structure for a fault tolerant aircraft control system because fault diagnosis of flight control systems is extremely important in obtaining healthy flight. An approach to detect and isolate aircraft sensor faults is proposed, and a new integrated structure for a fault tolerant aircraft control system is presented.

As disturbance and sensor faults are mixed together in a flight control system, it is difficult to isolate any fault from the disturbance. This paper proposes a robust unknown input observer for state estimation and fault detection as well as isolation using fuzzy logic.

The dedicated observer scheme (DOS) and generalized observer scheme (GOS) are used for fault detection and isolation in an observer-based approach. Using the DOS, it has been shown through simulation that sensor fault detection and isolation can be made, but here the threshold value must be well chosen; if not, the faulty sensor cannot be correctly isolated. On the other hand, the GOS is more usable and flexible than the DOS and allows isolation of faults more correctly and for a fuzzy logic-based controller to be used to realize fault isolation completely.

The fuzzy logic approach applied to the flight control system adds an important key for sensor fault isolation because it reduces the effect of false alarms and allows the identification of different kinds of sensor faults. The proposed approach can be used for similar systems.

The purpose of this article is to present a new application of pursuit‐based analysis for diagnosing rolling element bearing faults.

Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique‐based fault detection and identification. This paper presents a comparative study of both the basis and matching pursuits when applied to fault diagnosis of rolling element bearings using vibration analysis.

Fault features were extracted from vibration acceleration signals and subsequently fed to a feed forward neural network (FFNN) for classification. The classification rate and mean square error (MSE) were calculated to evaluate the performance of the intelligent diagnostic procedure. Results from the basis pursuit fault diagnosis procedure were compared with the classification result of a matching pursuit feature‐based diagnostic procedure. The comparison clearly illustrates that basis pursuit feature‐based fault diagnosis is significantly more accurate than matching pursuit feature‐based fault diagnosis in detecting these faults.

Intelligent diagnosis can reduce the reliance on experienced personnel to make expert judgements on the state of the integrity of machines. The proposed method has the potential to be extensively applied in various industrial scenarios, although this application concerned rolling element bearings only. The principles of the application are directly translatable to other parts of complex machinery.

This work presents a novel intelligent diagnosis strategy using pursuit features and feed forward neural networks. The value of the work is to ease the burden of making decisions on the integrity of plant through a manual program in condition monitoring and diagnostics particularly of complex pieces of plant.

The purpose of this paper is to apply grey statistical model to identify and classify live fault rupture.

Based on grey statistical mode, this paper uses eight faults' ripping speed observation data from 1997 to 2001, according to the grey statistics method for analysis, and recognizes active fault rupture situation. Using the conventional methods, namely taking all faults monitoring stations' average dislocation rate to analysis and make judgment, the average results are obtained.

The results show that the results are closer to reality because the grey statistical evaluation method has considered dislocation rate and other discrete factors.

The method exposed in the paper can be used to monitor and recognize live fault rupture in earthquake prediction.

According to the fault dislocation rate, this paper advances active fault rupture identification and classification method based on grey statistical model.

The purpose of this paper is to present a study and analysis of insulation failure inter‐turn fault in induction machines (IMs).

A time stepping finite element method (FEM) analysis is performed for the study of IM with inter‐turn fault and determining the machine parameters (self and mutual inductances) after occurring fault. A simple dynamic model for IM with inter‐turn fault is presented. The model parameters are obtained by FEM analysis. An experimental test is also carried out to verify the results.

The behavior of IM is studied under various insulation failure inter‐turn fault conditions and severity using FEM. The paper's results help the machine designers to improve the fault tolerance as well the overall design of the machine drive system. It can also be useful for predict and detection of fault in IM.

Predicting and detection of turn faults in IM are in industry very helpful because it avoids the fully damage of IM and it is more easy to repair the machine. Designing a fault tolerant IM is required in some applications for increasing the reliability.

By using FEM for studying the fault, the machine parameters which are calculated with FEM and the study's results are very precise and accurate because the flux fluctuation after occurring fault has been taken into account. On the other hand, the fault model is very fast, global and accurate. It can be used in model‐based health monitoring systems.

The purpose of this paper is to present a new nested rapidly‐exploring random tree (RRT) algorithm for fault tolerant motion planning of robotic manipulators.

Another RRT algorithm is nested within the general RRT algorithm. This second nested level is used to check whether the new sampled node in the first nested level is fault tolerant. If a solution can be found in the second nested RRT, the reduced manipulator after failures at the new sampled node can still fulfill the remaining task and this new sampled node is added into the nodes of RRT in the first level. Thus, the nodes in the first level RRT algorithm are all fault tolerant postures. The final trajectory joined by these nodes is also obviously fault tolerant. Besides fault tolerance, this new nested RRT algorithm also can fulfill some secondary tasks such as improvement of dexterity and obstacle avoidance. Sufficient simulations and experiments of this new algorithm on fault tolerant motion planning of robotic manipulators are implemented.

It is found that the new nested RRT algorithm can fulfill fault tolerance and some other secondary tasks at the same time. Compared to other existing fault tolerant algorithms, this new algorithm is more efficient.

The paper presents a new nested RRT algorithm for fault tolerant motion planning.

The purpose of this paper is to use Bayesian probability theory to analyze the software reliability model with multiple types of faults. The probability that all faults are detected and corrected after a series of independent software tests and correction cycles is presented. This in turn has a number of applications, such as how long to test a software, estimating the cost of testing, etc.

The use of Bayesian probabilistic models, when compared to traditional point forecast estimation models, provides tools for risk estimation and allows decision makers to combine historical data with subjective expert estimates. Probability evaluation is done both prior to and after observing the number of faults detected in each cycle. The conditions under which these two measures, the conditional and unconditional probabilities, are the same is also shown. Expressions are derived to evaluate the probability that, after a series of sequential independent reviews have been completed, no class of fault remains in the software system by assuming the prior distribution as Poisson and binomial.

From results in Sections 4 and 5 it can be observed that the conditional and unconditional probabilities are the same if the prior probability distribution is Poisson and binomial. In these cases the confidence that all faults are deleted is not a function of the number of faults observed during the successive reviews but it is a function of the number of reviews, the detection probabilities and the mean of the prior distribution. This is a remarkable result because it gives a circumstance in which the statistical confidence from a Bayesian analysis is actually independent of all observed data. From the result in Section 4 it can be seen that exponential formula could be used to evaluate the probability that no fault remains when a Poisson prior distribution is combined with a multinomial detection process in each review cycle.

The paper is part of research work for a PhD degree.

Reports how the theorem of the texture “tuned” mask was modified to solve some problems encountered in the automatic faults (including filling bars, oil stains, weft‐lacking and holes) detection and recognition of the plain woven fabrics. These problems are the faults of variable shapes and sizes, those of variable structure and the grey‐level differences in the faults of oil stains. The index of the “tuned” mask in the texture “tuned” mask theorem was modified to converge the variability of the faults, and to elongate the distances between each fault’s average texture energy so that the texture energy in normal texture and in faults can be confined to different fixed ranges. The results show that the optimum texture “tuned” mask found from the modified theorem of the texture “tuned” mask can be used satisfactorily to identify different faults due to structure, shapes and size variation. However, in the case of undertoned oil stains and lower density filling bars, this method may sometimes cause misidentification.

The purpose of this paper is to develop a proper tool for structural analysis and designing in near‐fault regions where the level of earthquake hazard is more and its nature is different from far‐field regions.

The paper uses near‐fault records and pulse extraction using wavelet analysis and designs a spectra concept.

The paper developed a proper design spectra for near‐fault structural analysis and design.

This paper using pulse extraction for developing a 3D response spectrum with paying attention to earthquake magnitude is new and final design spectra proposed is valuable.