Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.

Distinct Fault Analysis of Induction Motor Bearing Using Frequency Spectrum Determination and Support Vector Machine

Abstract: In modern industrial environment, the demand for condition monitoring and maintenance management for the induction motor has increased. Among all the components of the induction motor, bearing is the critical component and the fault occurring in it has to be considered as a major issue. Usually, the bearing fault can be detected by the vibrational analysis. However, this method has a disadvantage that location of the equipment is not always easily accessible, and also it is quite costly. Thus, in this paper, an experiment for detecting the fault in the bearing of a three phase induction motor is achieved by the frequency selection in the stator-current spectrum. Their feature was evaluated by the fast Fourier transform and the diagnosis was performed by a support vector machine. Experimental results were obtained considering two types of outer raceway bearing faults at different load conditions and promising results were obtained.

An intelligent condition-based maintenance platform for rotating machinery

Abstract: Highlights? A comprehensive system consisted of hardware and software components is proposed. ? Software component covers a wide range from signal processing to prognostics. ? This system has been verified in industrial application. Maintenance is of necessity for sustaining machinery availability and reliability in order to ensure productivity, product quality, on-time delivery, and safe working environment. The costly maintenance strategies such as corrective maintenance and scheduled maintenance have been progressively replaced by superior maintenance strategies in which condition-based maintenance (CBM) is one of the delegates. This strategy commonly consists of sequent modules such as data acquisition, signal processing, feature extraction and feature selection, condition monitoring, etc. However, approaches in literature which have been developed for each module and implemented for different applications are standalone instead of a comprehensive system. Furthermore, these approaches have been demonstrated in a laboratory environment without any industrial validations. For these reasons, an intelligent algorithm based CBM platform is proposed in this paper to be applied for rotating machinery easily and effectively. Subsequently, two case-studies are presented in order to evaluate the effectiveness of this platform in industrial applications.

Neural network based analysis system for vibration analysis and condition monitoring

Abstract: A system and a method for tracking long term performance of a vibrating body such as a gas turbine, includes a vibration sensor who time domain outputs are transformed to the frequency domain, using a fast Fourier transform processing. Frequency domain outputs are provided as inputs to a fuzzy adaptive resonance theory neural network. Outputs from the network can be coupled to an expert system for analysis, to display devices for presentation to an operator or are available for other control and information purposes.