In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite measurements and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides t...

Bearings play an important role in mechanical transmission. Many disasters are due to bearing faults. This has driven the need in research for early bearing fault detection. The goal is to extract the periodic impulse signals from the very noise signal which are indicative of a bearing fault. This is done by enhancing impulsive signals while suppressing other signals. This paper used a new meth...

Fault detection based on comparing a batch of data with a model of the system using the generalized likelihood ratio test is considered. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. There are two standard approaches to this problem. One is based on a parity space, where the influence of initial state is removed by projection, and t...

Centrifugal pumps are the most vital part of any process industry. A fault in centrifugal pump can affect imperative industrial processes. To ensure reliable operation pump, this paper proposes a novel automated health state diagnosis framework for that combines signal to time-frequency imaging technique and an Adaptive Deep Convolution Neural Network model (ADCNN). First, vibration signals cor...

In this paper, a robust algorithm for fault diagnosis of power system equipment based on a Failure-Sensitive Matrix (FSM) is presented. FSM is a dynamic matrix structure updated by multiple measurements (on-line) and test results (off-line) on the systems. The algorithm uses many different artificial intelligence (AI) and expert system (ES) methods for detecting the location of faults, emerging...

Abstract: This paper describes a fault detection and isolation (FDI) scheme performed on the benchmark problem of a ship propulsion system. The model used for the ship propulsion system is nonlinear, for which two types of additive sensor faults, an additive incipient fault, and a multiplicative parametric fault are simulated. The estimation of the fault severity is accomplished by using an ada...

Vibrodiagnosis helps in detecting incipient faults in rotating machines like pumps and generators. Early detection prevents from undesired breakdown of the machine and allows to schedule maintenance times. The application of neural networks in classification of the rotating machine condition has been described in this work. Different types of networks and methods on feature extraction was descr...

This paper brings up a novel method for detecting induction motor stator winding faults at an early stage. The contribution of the work comes from the delicate handling of motor vibration by applying envelope analysis, which makes it possible to capture electrical short-circuit signature in mechanical signals, even if the magnitude of the fault is fairly incipient. Conventional induction motor ...

With the widespread application of wind turbines, the Maintenance issue gets growing concern. Bearings play a critical role in industrial applications. It is necessary to effectively monitor their health status. The shock pulse method (SPM) can detect the incipient fault of bearings and prevent the fault consequence effectively. However, many researchers used laboratory data to validate the SPM...

Fault detection based on comparing a batch of data with a model of the system using the generalized likelihood ratio test is considered. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. There are two standard approaches to this problem. One is based on a parity space, where the influence of initial state is removed by projection, and t...