Journal ArticleDOI
Low speed bearing fault diagnosis using acoustic emission sensors
Reads0
Chats0
TLDR
In this paper, an acoustic emission (AE) based technique for low speed bearing fault diagnosis is presented, which starts with a heterodyne frequency reduction approach that samples AE signals at a rate comparable to vibration centered methodologies and then, the sampled AE signal is time synchronously resampled to account for possible fluctuations in shaft speed and bearing slippage.About:
This article is published in Applied Acoustics.The article was published on 2016-04-01. It has received 90 citations till now. The article focuses on the topics: Bearing (mechanical) & Fault (power engineering).read more
Citations
More filters
Journal ArticleDOI
Deep Learning Based Approach for Bearing Fault Diagnosis
TL;DR: In this paper, a deep learning-based approach for bearing fault diagnosis is presented, which preprocesses sensor signals using short-time Fourier transform (STFT) and uses an optimized deep learning structure, large memory storage retrieval (LAMSTAR) neural network, is built to diagnose the bearing faults.
Journal ArticleDOI
Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals
TL;DR: In this article, an early fault diagnostic technique based on acoustic signals was used for a single-phase induction motor, which can be also used for other types of rotating electric motors.
Journal ArticleDOI
A review of stochastic resonance in rotating machine fault detection
Siliang Lu,Qingbo He,Jun Wang +2 more
TL;DR: This study is committed to providing a comprehensive review of SR from history to state-of-the-art methods and finally to research prospects, along with the applications in rotating machine fault detection.
Journal ArticleDOI
Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery
TL;DR: A new signal feature extraction and fault diagnosis method for fault diagnosis of low-speed machinery by combining Statistic filter and wavelet package transform with moving-peak-hold method to extract features of a fault signal and special bearing diagnostic symptom parameters are defined to recognize fault types.
Journal ArticleDOI
Diagnosis of the three-phase induction motor using thermal imaging
Adam Glowacz,Z. Głowacz +1 more
TL;DR: The authors develop an original method of the feature extraction of thermal images MoASoID (Method of Areas Selection of Image Differences), which compares many training sets together and it selects the areas with the biggest changes for the recognition process.
References
More filters
Journal ArticleDOI
The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms
TL;DR: In this article, the use of the fast Fourier transform in power spectrum analysis is described, and the method involves sectioning the record and averaging modified periodograms of the sections.
Journal ArticleDOI
Rolling element bearing diagnostics—A tutorial
Robert B. Randall,Jérôme Antoni +1 more
TL;DR: This tutorial is intended to guide the reader in the diagnostic analysis of acceleration signals from rolling element bearings, in particular in the presence of strong masking signals from other machine components such as gears.
Journal ArticleDOI
Neural-network-based motor rolling bearing fault diagnosis
TL;DR: Simulation and real-world testing results obtained indicate that neural networks can be effective agents in the diagnosis of various motor bearing faults through the measurement and interpretation of motor bearing vibration signatures.
Journal ArticleDOI
Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks
TL;DR: This work presents a novel monitoring scheme applied to diagnose bearing faults that takes into account the detection of distributed defects, such as roughness, and analyzes the most significant statistical-time features calculated from vibration signal.
Journal ArticleDOI
Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis
TL;DR: Comparisons with other conventional methods, such as principal component analysis, local preserving projection, canonical correction analysis, maximum margin criterion, LDA, and marginal Fisher analysis, show the superiority of TR-LDA in fault diagnosis.