Techniques for Large, Slow Bearing Fault Detection
نویسندگان
چکیده
منابع مشابه
Novel Hilbert Huang Transform Techniques for Bearing Fault Detection
........................................................................................................................................... i Acknowledgements......................................................................................................................... ii Table of
متن کاملA new technique for bearing fault detection in the time-frequency domain
This paper presents a new Fast Kurtogram Method in the time-frequency domain using novel types of statistical features instead of the kurtosis. For this study, the problem of four classes for Bearing Fault Detection is investigated using various statistical features. This research is conducted in four stages. At first, the stability of each feature for each fault mode is investigated. Then, res...
متن کاملAn Enhanced Energy Operator for Bearing Fault Detection
This paper reports an enhanced energy operator (EEO) method to detect bearing faults. This new energy operator exploits both the interference handling capability of a differentiation step and the noise suppression nature of the integration process. All these elements, i.e., differentiation, integration and energy operator, are implemented by a simple formula in one step. The main advantages of ...
متن کاملWavelet Based Signal Demodulation Technique for Bearing Fault Detection
Diagnostics of rolling elements under varying operational conditions, where disturbances and other rotating elements have strong influence on correctness of analysis, requires engagement of advanced signal processing techniques. Extraction of signal components generated by bearing faults has been proven to be an exceptionally promising method for rolling element bearing fault detection. In this...
متن کاملFault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods
Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Prognostics and Health Management
سال: 2020
ISSN: 2153-2648,2153-2648
DOI: 10.36001/ijphm.2016.v7i1.2358