Online Bearing Fault Diagnosis Based on a Novel Multiple Data Streams Transmission Scheme

Abstract

Bearing faults are the most common failure modes in the rotating system. Vibration data from the rotating system carry important information, that is, characterization and diagnosis; therefore, the vast vibration signals collected from multiple sensors mounted in different sites are transmitted in a certain order for online fault diagnosis. However, due to the influence of transfer paths and noises, the sensitivities to the same fault signal of measured data streams are of significant differences, and signals containing weak sensitivity to the fault are likely to be transmitted preferentially while neglecting transmission order. Meanwhile, high volume vibration data greatly increase the transmission burden. These above-mentioned reasons dramatically reduce online diagnostic efficiency. Thus, fully considering the sensitive differences to the fault for multiple channels, how to transmit measured data streams of multiple sensors for timely online detecting the bearing failure is still a primary challenge. In order to solve this problem, a novel online bearing fault diagnosis method based on the multiple data streams transmission schemes (MDSTS) is proposed in this paper. Multiple sensors are numbered consecutively, and data streams from all channels are transmitted according to the preset order and transport protocol via a certain length at the beginning of diagnosis. Then, a fault sensitivity assessment model (FSAM) is established on maximum mean discrepancy (MMD) for transmitting the most sensitive data stream by calculating the distribution discrepancies between each channel's data streams and the historical datasets in the frequency domain, and then, the fault diagnosis model based on K-nearest neighbor (KNN) trained on historical datasets was used to evaluate the transmission scheme and acquire reliable diagnostic results via predicting performances of multiple and consecutive datablocks until all these exceed an alarm value. The extensive experiment results show that the proposed method can timely and accurately identify the bearing faults and outperforms obviously competitive approaches.

Smart Home Control and Management Based on Big Data Analysis

“…The data information flow generated in the operation of smart home mainly includes two types: control data information flow and state data information flow. The control data information flow refers to the control instructions sent by the smart client device to the home terminal device [ 32 , 33 ]; Status data information flow refers to the data information that the home terminal device transmits its own status information and collected environmental information to the smart client device, and then feeds back to the user through the interactive interface. Smart home data stream transmission is shown in Figure 7 .…”

Stray Flux Multi-Sensor for Stator Fault Detection in Synchronous Machines

“…To make a decision, the single mass function m r can be transformed into the probability measure BetP [23,24] defined by (5). In case of the faulty machine (B-3A), using Equation ( 5 BetP({n}) = 0 + 0.0001 2 = 0.00005 Therefore, we obtain BetP({y}) = 0.99995 probability for "yes" and BetP({n}) = 0.00005 for "no".…”

“…In many works, it has been shown that electrical machines are subject to mechanical issues common to most rotating machines [2,3], such as mechanical imbalance [4], bearing faults [5], or resonance [6]. However, electrical machines also have their own specific problems such as stator winding faults, demagnetization of magnets, or broken rotor bars, which will produce magnetic imbalances.…”

A wavelet packet transform-based deep feature transfer learning method for bearing fault diagnosis under different working conditions

“…Shen et al [31] proposed a new penalty domain selection machine (PDSM) transfer learning model for gearbox fault recognition. Tong et al [32] proposed Fault Sensitivity Assessment Model (FSAM) based on the Maximum Mean Difference (MMD) to transmit the most sensitive data stream, and then analyze the fault.…”