It has been proved that global and local structure are both important for process monitoring, but principal component analysis (PCA) and locality preserving projections (LPP) can not consider them simultaneously in the process of dimension reduction. This article proposes a novel method named local and global structure preserving projections with Bayes classification (LGSPP-Bayes). The original...

This paper puts together a collection of a number of previously proposed signal processing techniques to detect and diagnose faults in rolling element bearings. The collection of previously proposed signal processing algorithms contains two main phases. Phase one includes a surveillance and diagnosis stage using time, frequency and an envelope analysis over the full frequency bandwidth. The sec...

Detection of incipient (slowly developing) faults is crucial in automated maintenance problems where early detection of worn equipment is required. In this paper, a general framework for model-based fault detection and diagnosis of a class of incipient faults is developed. The changes in the system dynamics due to the fault are modeled as nonlinear functions of the state and input variables, wh...

This paper presents a method for fault detection and diagnosis of actuator loss of effectiveness for a quadrotor helicopter. This paper not only considers the detection of the actuator loss of effectiveness faults, but also addresses the diagnosis of the faults. The detection and estimation of the faults are performed by the Augmented Extended Kalman Filter. The faults are modelled as random wa...

Automatic control systems with sophisticated control algorithm can be very large and complex. In order to improve the automatic process control, it is important to develop fault diagnosis strategy. A hierarchical scheme of fault detection and isolation based on Decision Support System (DSS) is presented. For fault diagnosis, a knowledge based procedure is required. In addition to analytic sympt...

Biochemical processes are gaining industrial importance because of their inherent advantages like mild operating conditions and versatility of the processes which mean lower operating costs in terms of lesser energy requirements and a number of industrially important products that could be made from a general process setup. These features have made the biochemical processes the target of intens...

Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third chan...

This article presents a method for fault detection and diagnosis of stator inter-turn short circuitin three phase induction machines. The technique is based on the stator current and modelling in the dqframe using an Adaptive Neuro-Fuzzy artificial intelligence approach. The developed fault analysis method is illustrated using MATLAB simulations. The obtained results are promisingbased on the n...

this paper addresses the design of an observer-based fault diagnosis scheme, which is applied to some of the sensors and actuators of a wind turbine benchmark model. the methodology is based on a modified sliding mode observer (smo) that allows accurate reconstruction of multiple sensor or actuator faults occurring simultaneously. the faults are reconstructed using the equivalent output error i...

The signal in the nature ofsingularity is always caused by mechanical fault of CNC machine tool. It is important to recognize the singularity correctly for mechanical fault diagnosis. This paper deals with the wavelet t ransform and the relation between the modulus maxima and the singularity detection, and based on the excellent property of the Hermitian wavelet, poses the conception that appli...