Detection of a Helicopter Input Pinion Bearing Fault Using Interstitial Envelope Analysis
نویسندگان
چکیده
منابع مشابه
Bearing Fault Diagnosis Method Using Envelope Analysis and Euclidean Distance
Bearings are widely used in rotating machines. Its health status is a significant index to indicate whether machines run continually or not. Detecting the bearing faults timely is very important for the maintenance decision making. In this paper, a new fault diagnosis method based on envelope analysis and Euclidean Distance is developed. Envelope analysis is used to enable the fault frequencies...
متن کاملWavelet Analysis And Envelope Detection For Rolling Element Bearing Fault Diagnosis A Comparative Study
Envelope Detection (ED) is traditionally always used with Fast Fourier Transform (FFT) to identify the rolling element bearing faults. The inability of FFT to detect non-stationary signals makes Wavelet Analysis (WA) an alternative for machinery fault diagnosis as WA can detect both stationary and non-stationery signals. A comparative study of ED with FFT and WA techniques for bearing fault dia...
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 کاملA train bearing fault detection and diagnosis using acoustic emission
Article history: Received 6 September, 2015 Accepted 9 December 2015 Available online 9 December 2015 This paper provides a method of acoustic emission (AE) technique to detect a train bearing fault of tapered bearing unit (TBU). An approach is to utilize acoustic emission signals which were captured from piezoelectric transducer and processed using Fourier transform. The transformed signals ma...
متن کاملBearing Fault Detection Based on Maximum Likelihood Estimation and Optimized ANN Using the Bees Algorithm
Rotating machinery is the most common machinery in industry. The root of the faults in rotating machinery is often faulty rolling element bearings. This paper presents a technique using optimized artificial neural network by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (maximum likelihood estima...
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: The International Journal of Acoustics and Vibration
سال: 2006
ISSN: 2415-1408
DOI: 10.20855/ijav.2006.11.3198