Condition monitoring

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

A comparative study on vibration‐based condition monitoring algorithms for wind turbine drive trains

Abstract: The ability to detect and diagnose incipient gear and bearing degradation can offer substantial improvements in reliability and availability of the wind turbine asset. Considering the motivation for improved reliability of the wind turbine drive train, numerous research efforts have been conducted using a vast array of vibration-based algorithms. Despite these efforts, the techniques are often evaluated on smaller-scale test-beds, and existing studies do not provide a detailed comparison between the various vibration-based condition monitoring algorithms. This study evaluates a multitude of methods, including frequency domain and cepstrum analysis, time synchronous averaging narrowband and residual methods, bearing envelope analysis and spectral kurtosis-based methods. A full-scale baseline wind turbine drive train and a drive train with several gear and bearing failures are tested at the National Renewable Energy Laboratory (NREL) dynamometer test cell during the NREL Gear Reliability Collaborative Round Robin study. A tabular set of results is presented to highlight the ability of each algorithm to accurately detect the bearing and gear wheel component health. The results highlight that the cepstrum and the narrowband phase modulation signal were effective methods for diagnosing gear tooth problems, whereas bearing envelope analysis could confidently detect most of the bearing-related failures. Copyright © 2013 John Wiley & Sons, Ltd.

Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals.

Abstract: In the condition monitoring of roller bearings, the measured signals are often compounded due to the unknown multi-vibration sources and complex transfer paths. Moreover, the sensors are limited in particular locations and numbers. Thus, this is a problem of underdetermined blind source separation for the vibration sources estimation, which makes it difficult to extract fault features exactly by ordinary methods in running tests. To improve the effectiveness of compound fault diagnosis in roller bearings, the present paper proposes a new method to solve the underdetermined problem and to extract fault features based on variational mode decomposition. In order to surmount the shortcomings of inadequate signals collected through limited sensors, a vibration signal is firstly decomposed into a number of band-limited intrinsic mode functions by variational mode decomposition. Then, the demodulated signal with the Hilbert transform of these multi-channel functions is used as the input matrix for independent component analysis. Finally, the compound faults are separated effectively by carrying out independent component analysis, which enables the fault features to be extracted more easily and identified more clearly. Experimental results validate the effectiveness of the proposed method in compound fault separation, and a comparison experiment shows that the proposed method has higher adaptability and practicability in separating strong noise signals than the commonly-used ensemble empirical mode decomposition method.

Studies on Bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features

Abstract: Various methods of tool condition monitoring techniques are used to control the tool wear during machining in CNC machine tools. Based on a continuous acquisition of signals with sensor systems it is possible to classify certain wear parameters by the extraction of features. Data mining approach is used to probe into the structural information hidden in the signals acquired. This paper discusses machine tool condition monitoring of carbide tipped tool by using Naive Bayes and Bayes Net classifiers and compares the results of histogram features with the statistical features to establish better classification among the two. The vibration signals are acquired for various tool conditions like tool-good condition, tip-breakage, etc. The effort is to bring out the better feature-classifier combine. The results are discussed.

Condition monitoring of steam turbines by performance analysis

Abstract: Many power generation steam turbine generators today are required in service well beyond their intended lifetimes. Dismantling for inspection is expensive, and owners need to consider all relevant information in making the decision. Application of condition monitoring in all the applicable methods is justified, with each showing different degradation modes. Performance analysis is less well publicised, yet unlike vibration analysis and oil debris analysis, it will show conditions which reduce machine efficiency and output, such as deposits on blades and erosion of internal clearances. Data obtained from tests before and after overhaul also reveal whether any restorative work achieved the expected improvements in performance. The paper outlines, with examples, some condition monitoring techniques that have contributed to retaining some large fossil machines in service for up to 17 years without opening high‐pressure sections.