Design of a Wireless Sensor Node for Vibration Monitoring of Industrial Machinery

2016 ◽  
Vol 6 (2) ◽  
pp. 639 ◽  
Author(s):  
Alaa Abdulhady Jaber ◽  
Robert Bicker
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Industrial Robot ◽  
Industrial Applications ◽  
Base Station ◽  
Fault Detection And Diagnosis ◽  
Vibration Monitoring ◽  
Wireless Sensor Node ◽  
Vibration Signal Analysis ◽  
Detection And Diagnosis

Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis.

scholarly journals Design of a Wireless Sensor Node for Vibration Monitoring of Industrial Machinery

2016 ◽  
Vol 6 (2) ◽  
pp. 639
Author(s):  
Alaa Abdulhady Jaber ◽  
Robert Bicker
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Industrial Robot ◽  
Industrial Applications ◽  
Base Station ◽  
Fault Detection And Diagnosis ◽  
Vibration Monitoring ◽  
Wireless Sensor Node ◽  
Vibration Signal Analysis ◽  
Detection And Diagnosis

Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis.

Fault Detection and Diagnosis of Industrial Robot Based on Power Consumption Modeling

2020 ◽  
Vol 67 (9) ◽  
pp. 7929-7940 ◽  
Author(s):  
Ahmad H. Sabry ◽  
Farah Hani Nordin ◽  
Ameer H. Sabry ◽  
Mohd Zainal Abidin Ab Kadir
Keyword(s):  
Fault Detection ◽  
Power Consumption ◽  
Industrial Robot ◽  
Fault Detection And Diagnosis ◽  
Detection And Diagnosis

scholarly journals Early Fault Detection in Bearing with Fault Seeded on Outer Raceway at Three Different Position: Orthogonal, Centered and Opposite

2019 ◽  
Vol 8 (4) ◽  
pp. 6448-6453
Keyword(s):  
Fault Detection ◽  
Time Domain ◽  
Low Voltage ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Fault Detection And Diagnosis ◽  
Economic Losses ◽  
Detection And Diagnosis ◽  
Early Fault Detection ◽  
The Time Domain

Rotating machine such as a small low voltage motor or a power plant generator is an essential asset to the industrial applications. The execution and efficiency of these rotating machines are being reduced due to faulty rotating machinery parts. The faulty parts also generate various forces, thus increases the amplitude of vibration as well as energy consumption. Early fault detection and diagnosis have been widely used with various methods as they were able to reduce accidents and machine breakdowns along with economic losses. This study aims to present the faulty bearings which were seeded in the bearings. The fault size are ranging from 0.007 inches to 0.021 inches in diameter. Among the methods, vibration signal data is one of the champions. In this study, early fault detection was focused on bearing using the time domain technique and the data were analyzed. Particularly, the fault was introduced on the outer raceway at three different positions; orthogonal (3 o’clock), centered (6 o’clock) and opposite (12 o’clock). The MATLAB software was used to determine the time domain parameters, comprising of the standard deviation, Root Mean Square (RMS), skewness and shape factor as the representation of the best reflection of the failure. The time domain parameters for healthy and faulty bearing were plotted and compared in graphical presentation. The result shows all the four parameters have greater value in contrast with the healthy bearing value except for skewness data in the opposite (12 o’clock) position.

Class Incremental Fault Detection and Diagnosis With Unlabeled Condition Monitoring Data

2020 ◽  
Author(s):  
Xiaomeng Peng ◽  
Xiaoning Jin ◽  
Shiming Duan ◽  
Chaitanya Sankavaram
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Failure Modes ◽  
Fault Detection And Diagnosis ◽  
Testing Data ◽  
Supervised Classifiers ◽  
On Line ◽  
Complete Failure ◽  
Complete Set ◽  
Detection And Diagnosis

Abstract Data-driven methods for fault detection and diagnostics (FDD) require a large amount of labeled data and knowledge about complete failure modes set to train a reliable classifier as well as require the same label space in an online testing phase. Typical supervised classifiers in FDD can only predict precedented faults, limiting their performance in identifying unprecedented failure modes in on-line testing data. In addition, in most applications, it may be expensive and time-consuming to obtain sufficient labeled samples. This study focuses on fault detection and diagnosis without sufficient labels or prior knowledge of the complete set of failure modes. This paper proposes a novel FDD framework using active learning and semi-supervised learning to detect both precedented and unprecedented failures with minimum labeling effort. The effectiveness of proposed approach is demonstrated and validated using a synthetic condition monitoring dataset.

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