Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

2011 ◽  
Vol 3 (4) ◽  
pp. 67-83 ◽  
Author(s):  
Cyprian F. Ngolah ◽  
Ed Morden ◽  
Yingxu Wang
Keyword(s):  
Performance Indicators ◽  
High Demand ◽  
Rotating Machine ◽  
Fault Recognition ◽  
Machine Operators ◽  
System Vibration ◽  
Machine Health ◽  
Condition Based Monitoring ◽  
Industrial Machine ◽  
Previous Training

Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: (a) access to the machines on site is sometimes impracticable, and (b) the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

2013 ◽  
pp. 158-172 ◽  
Author(s):  
Cyprian F. Ngolah ◽  
Ed Morden ◽  
Yingxu Wang
Keyword(s):  
Machine Condition ◽  
Vibration Signals ◽  
Rotating Machine ◽  
Fault Recognition ◽  
Machine Operators ◽  
System Vibration ◽  
Machine Health ◽  
Condition Based Monitoring ◽  
Industrial Machine ◽  
Previous Training

Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: (a) access to the machines on site is sometimes impracticable, and (b) the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

Machine Fault Detection Using Genetic Programming

2005 ◽  
Author(s):  
B. Samanta
Keyword(s):  
Fault Detection ◽  
Genetic Programming ◽  
Vibration Data ◽  
Rotating Machine ◽  
Fault Recognition ◽  
Machine Fault ◽  
Artificial Neural Network Ann ◽  
Monitoring And Diagnostics ◽  
The Time Domain ◽  
Selection Of

Applications of genetic programming (GP) include many areas. However applications of GP in the area of machine condition monitoring and diagnostics is very recent and yet to be fully exploited. In this paper, a study is presented to show the performance of machine fault detection using GP. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GP for two class (normal or fault) recognition. The number of features and the features are automatically selected in GP maximizing the classification success. The results of fault detection are compared with genetic algorithm (GA) based artificial neural network (ANN)- termed here as GA-ANN. The number of hidden nodes in the ANN and the selection of input features are optimized using GAs. Two different normalization schemes for the features have been used. For each trial, the GP and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GP and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers with GP and GA based selection of features.

scholarly journals Condition Monitoring of Forward Curved Centrifugal Blower Using Coast Down Time Analysis

2010 ◽  
Vol 2010 ◽  
pp. 1-12
Author(s):  
G. R. Rameshkumar ◽  
B. V. A. Rao ◽  
K. P. Ramachandran
Keyword(s):  
Condition Monitoring ◽  
Health Condition ◽  
Centrifugal Blower ◽  
Rotating Machine ◽  
Monitoring Parameter ◽  
Shaft Misalignment ◽  
Sustained Operation ◽  
Mechanical Faults ◽  
Machine Health ◽  
Reduction Percentage

Mechanical malfunctions such as, rotor unbalance and shaft misalignment are the most common causes of vibration in rotating machineries. Vibration is the most widely used parameter to monitor and asses the machine health condition. In this work, the Coast Down Time (CDT), which is an indicator of faults, is used to assess the condition of the rotating machine as a condition monitoring parameter. CDT is the total time taken by the system to dissipate the momentum acquired during sustained operation. Extensive experiments were conducted on Forward Curved Centrifugal Blower Test Rig at selected cutoff speeds for several combinations of combined horizontal and vertical parallel misalignment, combined parallel and angular misalignment, as well as for various unbalance conditions. As mechanical faults increase, a drastic decrease in CDT is found and this is represented as CDT reduction percentage. A specific correlation between the CDT reduction percentage, level of mechanical faults, and rotational cutoff speeds is observed. The results are analyzed and compared with vibration analysis for potential use of CDT as one of the condition monitoring parameter.

scholarly journals Investigating Human-Machine Interfaces’ Efficiency in Industrial Machinery and Equipment: A Kenyan Context

2021 ◽  
Vol 11 (4) ◽  
pp. 1-8
Author(s):  
Silas M. Nzuva
Keyword(s):  
User Interfaces ◽  
Working Environment ◽  
First Century ◽  
Physical Systems ◽  
Human Machine Interfaces ◽  
Hands On ◽  
Machine Operators ◽  
Operation Control ◽  
Workforce Productivity ◽  
Industrial Machine

The twenty-first century has seen a vast technological revolution characterized by the development of cyber-physical systems, integration of things, and new and computationally improved machines and systems. However, there have been seemingly little strides in the development of user interfaces, specifically for industrial machines and equipment. The aim of this study was to assess the efficiency of the human-machine interfaces in the Kenyan context in providing a consistent and reliable working environment for industrial machine operators. The researcher employed a convenient purposive sampling to select 15 participants who had at least two years of hands-on experience in machines operation, control, or instrumentation. The results of the study are herein presented, including the recommendations to enhance workforce productivity and efficiency.

scholarly journals Condition-Based Monitoring and Maintenance: State of the Art Review

2022 ◽  
Vol 12 (2) ◽  
pp. 688
Author(s):  
Ahad Ali ◽  
Abdelhakim Abdelhadi
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Manufacturing Firms ◽  
Condition Based Maintenance ◽  
Operations And Maintenance ◽  
Practical Development ◽  
Great Pressure ◽  
Machine Health ◽  
And Performance ◽  
Condition Based Monitoring

Manufacturing firms face great pressure to reduce downtime as well as maintenance costs. Condition-based maintenance (CBM) can be used to effectively manage operations and maintenance by monitoring detailed machine health information. CBM policies and the development of the mathematical models have been growing recently. This paper provides a review of the theoretical and practical development in the field of condition-based maintenance and its current advancements. Standard CBM platform could make it effective and efficient in implementation and performance improvement.

scholarly journals Actions Speak Louder than Words: Stop Talking-Do! The Role of Social Psychology to turn Talkers into Doers to Nourish Organizational Behavior

2018 ◽  
Vol 8 (4) ◽  
pp. 25
Author(s):  
Eddie John Fisher ◽  
Yorkys Santana González ◽  
Eddie Fisher
Keyword(s):  
Social Psychology ◽  
Cultural Diversity ◽  
Organizational Behavior ◽  
Performance Indicators ◽  
High Demand ◽  
Performance Improvements ◽  
Potential Benefits ◽  
The Subject ◽  
Practical Experiences

Effective and capable people are in high demand amongst businesses and educational establishments. Companies are not getting the best out of their people. It appears that there is a gap between what people are saying they are going to do and what they actually do. This research, based on a combination of what is already known about the subject matter under investigation and the practical experiences of research participants, investigated how this organizational behavior gap could be closed and what the potential benefits would be for both individuals and companies. The main focus of this research was to identify effective solutions how talkers could become doers and how talkers would benefit from adopting and applying the suggested recommendations how changes in attitude/behaviors and routines could lead to improved action-based capabilities. The outcome of this research suggests that the contributions made by this research can be measured through appropriate Key Performance Indicators (KPIs) to measure and demonstrate the effectiveness of considered performance improvements. It is suggested that the contributions from this research can be applied universally provided that cultural diversity is taken into consideration. 

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