Abstract
<span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">Machine fault diagnosis systems need to collect and transmit dynamic signals, like vibration and current, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. Large amounts of transmission data will </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">increase the energy consumption and </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">shorten the lifetime of energy-constrained IWSN node</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">s as well</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">.</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">To address th</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">e</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">s</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">e</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US"> tension</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">s</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US"> when implementing machine fault diagnosis applications in </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">IWSNs</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">, this paper proposes a</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">n</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">energy efficient </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.</span>
Highlights
In recent decades, many novel machine fault diagnosis approaches have been proposed to prevent unexpected catastrophic machine failures and reduce the related economic loss due to these faults[1]
We have previously described an industrial wireless sensor networks (IWSNs) with on-sensor fault feature extraction using FFT for motor vibration signal and on-sensor fault diagnosis using artificial neural networks (ANN) in [8]
This paper explores the feasibility of using IWSNs with on-sensor Wavelet Transform (WT) and support vector machine (SVM) for fault feature extraction and fault diagnosis, compares the effectiveness of onsensor fault feature extraction using various mother wavelets, and quantifies the node energy cost of the proposed on-sensor fault diagnosis approach
Summary
Many novel machine fault diagnosis approaches have been proposed to prevent unexpected catastrophic machine failures and reduce the related economic loss due to these faults[1]. Compared with other wavelet-based classification schemes, this work uses a new, smaller set of features which are the signal energies at each wavelet decomposition level This significantly reduces the number of features, which allows for the use of fewer inputs to the classifier, again reducing the computational burden for on-sensor computation on the sensor node. The paper presents a detailed comparison of the energy consumption for raw data transmission versus on-sensor classification and shows a small benefit for the latter This is in addition to the other benefits of autonomous condition monitoring, such as an immediate ability to respond to faults, compared to a solution which depends on a remote diagnosis with increased latency and dependence on reliable radio communications.
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