Spindle Condition Monitoring With a Smart Vibration Sensor and an Optimized Deep Neural NetworkSource: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems:;2023:;volume( 006 ):;issue: 002::page 21003-1DOI: 10.1115/1.4056616Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: This article presents a spindle condition monitoring methodology using a low-power smart vibration sensor and a near real-time deep neural network (DNN) classifier. The most frequent spindle failures, such as imbalance, ingression, and evidence of a crash with the workpiece, are analyzed in this study. Experiments were designed to induce various failure events to monitor the spindle behavior using conventional vibration, current and temperature sensors, and an intelligent vibration sensor. The smart sensor is a device with internal signal processing identifying eight dominant frequencies and the amplitude/power distributions. It requires low power and generates narrow bandwidth messages that can be communicated wirelessly. A Fog device and a test plan are designed to monitor and store a dataset needed to train a DNN classifier. The Fog device generates temperature, current, and vibration signals from sensors connected to the spindle and sends them to data storage in the cloud. The signals were analyzed using both conventional vibration analysis and Artificial Intelligence-based classifiers. Metrics such as crest factor, skewness, kurtosis, and overall enveloping were used to assess their ability to identify the failure condition. The data from the smart sensor are used to train an optimized DNN, and the spindle defect classification performance is measured. With 960 data points per failure mode and training data taken over 960 min of operation, the optimized DNNs can classify the spindle states with an accuracy of 98%. The study shows real-time spindle condition classification feasibility over long periods using inexpensive and low-power smart vibration sensors.
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contributor author | Oh, Louis | |
contributor author | Pitz, Emil | |
contributor author | Pochiraju, Kishore | |
date accessioned | 2023-11-29T19:32:45Z | |
date available | 2023-11-29T19:32:45Z | |
date copyright | 1/31/2023 12:00:00 AM | |
date issued | 1/31/2023 12:00:00 AM | |
date issued | 2023-01-31 | |
identifier issn | 2572-3901 | |
identifier other | nde_6_2_021003.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4294850 | |
description abstract | This article presents a spindle condition monitoring methodology using a low-power smart vibration sensor and a near real-time deep neural network (DNN) classifier. The most frequent spindle failures, such as imbalance, ingression, and evidence of a crash with the workpiece, are analyzed in this study. Experiments were designed to induce various failure events to monitor the spindle behavior using conventional vibration, current and temperature sensors, and an intelligent vibration sensor. The smart sensor is a device with internal signal processing identifying eight dominant frequencies and the amplitude/power distributions. It requires low power and generates narrow bandwidth messages that can be communicated wirelessly. A Fog device and a test plan are designed to monitor and store a dataset needed to train a DNN classifier. The Fog device generates temperature, current, and vibration signals from sensors connected to the spindle and sends them to data storage in the cloud. The signals were analyzed using both conventional vibration analysis and Artificial Intelligence-based classifiers. Metrics such as crest factor, skewness, kurtosis, and overall enveloping were used to assess their ability to identify the failure condition. The data from the smart sensor are used to train an optimized DNN, and the spindle defect classification performance is measured. With 960 data points per failure mode and training data taken over 960 min of operation, the optimized DNNs can classify the spindle states with an accuracy of 98%. The study shows real-time spindle condition classification feasibility over long periods using inexpensive and low-power smart vibration sensors. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Spindle Condition Monitoring With a Smart Vibration Sensor and an Optimized Deep Neural Network | |
type | Journal Paper | |
journal volume | 6 | |
journal issue | 2 | |
journal title | Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems | |
identifier doi | 10.1115/1.4056616 | |
journal fristpage | 21003-1 | |
journal lastpage | 21003-10 | |
page | 10 | |
tree | Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems:;2023:;volume( 006 ):;issue: 002 | |
contenttype | Fulltext |