Condition monitoring

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

A Nonintrusive and In-Service Motor Efficiency Estimation Method using Air-Gap Torque with Considerations of Condition Monitoring

Abstract: Energy usage evaluation and condition monitoring for electric machines are important in industry for overall energy savings. They are often expected to be implemented in an integrated product because of many common requirements such as data collection. Because of the uninterrupted characteristic of industrial processes, traditional methods defined in IEEE Standard 112 cannot be used for these in-service motors. This paper proposes a truly nonintrusive method for in-service motor efficiency estimation based on air-gap torque using only motor terminal quantities and nameplate information, with special considerations of motor condition monitoring requirements. Rotor speed and stator resistance, the stumbling blocks of most in-service testing methods, are extracted from motor input currents instead of being measured. The no-load test, which is required for calculating the rotational loss and core loss, is eliminated by using empirical values. Stray-load loss is assumed according to the motor horse power as suggested in IEEE Standard 112. Finally, the proposed method is validated by testing three induction motors with different configurations. Experimental results show that the proposed method can estimate motor efficiencies with less than 2% errors under normal load conditions.

Learning methodology for failure detection and accommodation

Abstract: A major goal of intelligent control systems is to achieve high performance with increased reliability, availability, and automation of maintenance procedures. In order to achieve fault tolerance in dynamical systems many algorithms have been developed during the past two decades. Fault diagnosis and accommodation methods have traditionally been based on linear modeling techniques, which restricts the type of practical failure situations that can be modeled. This article presents a learning methodology for failure detection and accommodation. The main idea behind this approach is to monitor the physical system for any off-nominal behavior in its dynamics using nonlinear modeling techniques. The principal design tool used is a generic function approximator with adjustable parameters, referred to as online approximator. Examples of such structures include traditional approximation models such as polynomials and splines as well as neural networks topologies such as sigmoidal multilayer networks and radial basis function networks. Stable learning methods are developed for monitoring the dynamical system. The nonlinear modeling nature and learning capability of the estimator allow the output of the online approximator to be used not only for detection but also for identification and accommodation of system failures. Simulation studies are used to illustrate the learning methodology and to gain intuition into the effect of modeling uncertainties on the performance of the fault diagnosis scheme. >

Natural roller bearing fault detection by angular measurement of true instantaneous angular speed

Abstract: The challenge in many production activities involving large mechanical devices like power transmissions consists in reducing the machine downtime, in managing repairs and in improving operating time. Most online monitoring systems are based on conventional vibration measurement devices for gear transmissions or bearings in mechanical components. In this paper, we propose an alternative way of bearing condition monitoring based on the instantaneous angular speed measurement. By the help of a large experimental investigation on two different applications, we prove that localized faults like pitting in bearing generate small angular speed fluctuations which are measurable with optical or magnetic encoders. We also emphasize the benefits of measuring instantaneous angular speed with the pulse timing method through an implicit angular sampling which ensures insensitivity to speed fluctuation. A wide range of operating conditions have been tested for the two applications with varying speed, load, external excitations, gear ratio, etc. The tests performed on an automotive gearbox or on actual operating vehicle wheels also establish the robustness of the proposed methodology. By the means of a conventional Fourier transform, angular frequency channels kinematically related to the fault periodicity show significant magnitude differences related to the damage severity. Sideband effects are evidently seen when the fault is located on rotating parts of the bearing due to load modulation. Additionally, slip effects are also suspected to be at the origin of enlargement of spectrum peaks in the case of double row bearings loaded in a pure radial direction.

Multisensor Wireless System for Eccentricity and Bearing Fault Detection in Induction Motors

Abstract: This paper presents a stand-alone multisensor wireless system for continuous condition monitoring of induction motors. The proposed wireless system provides a low-cost alternative to expensive condition monitoring technology available through dedicated current signature analysis or vibration monitoring equipment. The system employs multiple sensors (acoustic, vibration, and current) mounted on a common wireless platform. The faults of interest are static and dynamic air-gap eccentricity, bearing damage, and their combinations. The Hilbert-Huang transform of vibration data and power spectral density of current and acoustic signals are used as the features in a hierarchical classifier. The proposed wireless system can distinguish a faulty motor from a healthy motor with a probability of 99.9% of correct detection and less than 0.1% likelihood of false alarm. It can also discriminate between different fault categories and severity with an average accuracy of 95%.