Transformers are important devices in power supply system. Since the testing data in fault diagnosis of transformers have features such as high dimension, uncertainty and incompleteness, existing fault diagnosis methods are incapable of dealing with the high-dimensional and abnormal data, and the diagnosis precision needs further improvement. This paper innovatively proposes a fault diagnosis m...

Different test sequence selection methods, namely, the Dmethod, C-method, W-method, T-method, U-method, Uvmethod and the Wp-method, are reviewed and analysed for their fault detection capabilities. We show that the C-method and the Uv-method do not provide complete fault coverage. These seven methods are formally analysed for their fault diagnosis capabilities under single fault assumption. Amo...

This paper presents an offline fault diagnosis method for industrial gas turbines in a steady-state. Fault diagnosis plays an important role in the efforts for gas turbine owners to shift from preventive maintenance to predictive maintenance, and consequently to reduce the maintenance cost. Ever since its birth, numerous techniques have been researched in this field, yet none of them is complet...

of-the-art chip designs to improve test quality and reduce the cost of test development and application. Despite such benefits, designers have not adopted BIST as the primary test methodology. Fault diagnosis in a BIST environment is problematic because only limited information is available in a compact signature like that produced with BIST. Previous techniques have focused on extracting infor...

One of the major challenges in robotic arms is to diagnosis sensor fault. To address this challenge, this paper presents an LPV approach. Initially, the dynamics of a two-link manipulator is modelled with a polytopic linear parameter varying structure and then by using a descriptor system approach and a robust design of a suitable unknown input observer by means of pole placement method along w...

Gear and bearing faults are the main causes of gearbox failure. Till now, incipient fault diagnosis of these two components has been a problem and needs further research. In this context, it is found that Lucy–Richardson deconvolution (LRD) proved to be an excellent tool to enhance fault diagnosis in rolling element bearings and gears. LRD’s good identification capabilities of fault frequencies...

This article presents a model-based fault diagnosis method to detect and isolate faults in the robot arm control system. The proposed algorithm is composed functionally of three main parts: parameter estimation, fault detection, and isolation. When a change in the system occurs, the errors between the system output and the estimated output cross a predetermined threshold, and once a fault in th...

This paper presents a new method to perform fault diagnosis for data-correlation based process monitoring. As alternative to the traditional contribution plot method, reconstruction-based contribution of fault detection indices is proposed. The monitored indices are SPE, T 2 and a combined index φ. The lack of diagnosability of traditional contributions is analyzed for the case of single sensor...

This paper outlines a hybrid approach of neuro-fuzzy based learning and classification approach based on the online learning systems. The effect of fault diagnosis for the suggested fault location tool is evaluated over the conventional fault diagnosis based approaches. The method of fault location based on the conventional offline neuro controller approach is compared with the suggested hybrid...

In this paper we describe the concept of physical impossibility as an alternative to the specification of fault models. These axioms can be used to exclude impossible diagnoses similar to fault models. We show for Horn clause theories while the complexity of finding a first diagnosis is worst-case exponential for fault models, it is polynomial for physical impossibility axioms. Even for the cas...