scholarly journals Mathematical Validation of Experimentally Optimised Parameters Used in a Vibration-Based Machine-Learning Model for Fault Diagnosis in Rotating Machines

Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 155
Author(s):  
Natalia Espinoza Sepulveda ◽  
Jyoti Sinha
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Fe Model ◽  
Rotating Machines ◽  
Fe Method ◽  
Industrial Systems ◽  
Machine Learning Model ◽  
Vibration Responses ◽  
Diagnosis Model ◽  
Experimental Rig

Mathematical models have been widely used in the study of rotating machines. Their application in dynamics has eased further research since they can avoid time-consuming and exorbitant experimental processes to simulate different faults. The earlier vibration-based machine-learning (VML) model for fault diagnosis in rotating machines was developed by optimising the vibration-based parameters from experimental data on a rig. Therefore, a mathematical model based on the finite-element (FE) method is created for the experimental rig, to simulate several rotor-related faults. The generated vibration responses in the FE model are then used to validate the earlier developed fault diagnosis model and the optimised parameters. The obtained results suggest the correctness of the selected parameters to characterise the dynamics of the machine to identify faults. These promising results provide the possibility of implementing the VML model in real industrial systems.

scholarly journals Blind Application of Developed Smart Vibration-Based Machine Learning (SVML) Model for Machine Faults Diagnosis to Different Machine Conditions

2020 ◽  
Author(s):  
Natalia F. Espinoza Sepúlveda ◽  
Jyoti K. Sinha
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Operating Conditions ◽  
Machine Vibration ◽  
Identical Machines ◽  
Vibration Responses ◽  
Faults Diagnosis ◽  
Diagnosis Model ◽  
Machine Speed

Abstract Purpose The development and application of intelligent models to perform vibration-based condition monitoring in industry seems to be receiving attention in recent years. A number of such research studies using the artificial intelligence, machine learning, pattern recognition, etc., are available in the literature on this topic. These studies essentially used the machine vibration responses with known machine faults to develop smart fault diagnosis models. These models are yet to be tested for all kinds of machine faults and/or different operating conditions. Therefore, the purpose is to develop a generic machine faults diagnosis model that can be applied blindly to any identical machines with high confidence level in accuracy of the predictions. Methods In this paper, a supervised smart fault diagnosis model is developed. This model is developed using the available measured vibration responses for the different rotor faults simulated on an experimental rotating rig operating at a constant speed. The developed smart vibration-based machine learning (SVML) model is then blindly tested to identify the healthy and faulty conditions of the rig when operating at different speeds. Results and conclusions Several scenarios are proposed and examined during the development of the SVML model. It is observed that scenario of the vibration measurements simultaneously from all bearings from a machine is capable to fully map the machine dynamics in the VML model. Therefore, this developed when applied blindly to the sets of data at a different machine speed, the results are observed to be encouraging. The results clearly show a possibility for a centralised vibration-based condition monitoring (CVCM) model for identical machines operating at different rotating speeds.

Vibration-Based Early Crack Diagnosis With Machine Learning for Spur Gears

2020 ◽  
Author(s):  
Fatih Karpat ◽  
Ahmet Emir Dirik ◽  
Onur Can Kalay ◽  
Oğuz Doğan ◽  
Burak Korcuklu
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
High Speed ◽  
Crack Detection ◽  
Power Transmission ◽  
Calculated Data ◽  
Design Parameters ◽  
Tooth Stiffness ◽  
Vibration Responses ◽  
Root Crack

Abstract Gear mechanisms are one of the most significant components of the power transmission systems. Due to increasing emphasis on the high-speed, longer working life, high torques, etc. cracks may be observed on the gear surface. Recently, Machine Learning (ML) algorithms have started to be used frequently in fault diagnosis with developing technology. The aim of this study is to determine the gear root crack and its degree with vibration-based diagnostics approach using ML algorithms. To perform early crack detection, the single tooth stiffness and the mesh stiffness calculated via ANSYS for both healthy and faulty (25-50-75-100%) teeth. The calculated data transferred to the 6-DOF dynamic model of a one-stage gearbox, and vibration responses was collected. The data gathered for healthy and faulty cases were evaluated for the feature extraction with five statistical indicators. Besides, white Gaussian noise was added to the data obtained from the 6-DOF model, and it was aimed at early fault diagnosis and condition monitoring with ML algorithms. In this study, the gear root crack and its degree analyzed for both healthy and four different crack sizes (25%-50%-75%-100%) for the gear crack detection. Thereby, a method was presented for early fault diagnosis without the need for a big experimental dataset. The proposed vibration-based approach can eliminate the high test rig construction costs and can potentially be used for the evaluation of different working conditions and gear design parameters. Therefore, catastrophic failures can be prevented, and maintenance costs can be optimized by early crack detection.

scholarly journals Optimization of a Dynamic Fault Diagnosis Model Based on Machine Learning

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 65065-65077 ◽  
Author(s):  
Shigang Zhang ◽  
Xu Luo ◽  
Yongmin Yang ◽  
Long Wang ◽  
Xiaofei Zhang
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Model Based ◽  
Diagnosis Model ◽  
Dynamic Fault Diagnosis

scholarly journals Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 66
Author(s):  
Natalia Espinoza Sepulveda ◽  
Jyoti Sinha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Research Effort ◽  
Data Driven ◽  
Rotating Machines ◽  
Operation Conditions ◽  
Machine Learning Model ◽  
Identical Machines ◽  
Faults Diagnosis ◽  
Vibration Parameters

Artificial intelligence (AI)-based machine learning (ML) models seem to be the future for most of the applications. Recent research effort has also been made on the application of these AI and ML methods in the vibration-based faults diagnosis (VFD) in rotating machines. Several research studies have been published over the last decade on this topic. However, most of the studies are data driven, and the vibration-based ML (VML) model is generally developed on a typical machine. The developed VML model may not predict faults accurately if applied on other identical machines or a machine with different operation conditions or both. Therefore, the current research is on the development of a VML model by optimising the vibration parameters based on the dynamics of the machine. The developed model is then blindly tested at different machine operation conditions to show the robustness and reliability of the proposed VML model.

Bispectrum: A Tool for Distinguishing Different Faults in Rotating Machine

2012 ◽  
Author(s):  
Keri Elbhbah ◽  
Jyoti K. Sinha
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
State Of The Art ◽  
Ball Bearings ◽  
Rotating Machines ◽  
Rotating Machine ◽  
Current State ◽  
The Future ◽  
Experimental Rig ◽  
Initial Results

The current state-of-the-art in vibration-based condition monitoring of rotating machines requires a number of vibration transducers at each bearing pedestal of a rotating machine to identify any faults, in the machine. In this paper, the use of the bispectrum has been proposed for fault diagnosis in rotating machines. The reason for this is that it may reduce the number of vibration transducers at each bearing pedestal in rotating machines in the future. The paper presents a comparison of the bispectrum results for four cases, namely; Healthy, Misaligned shaft, Crack Shaft and Shaft Rub on an experimental rig consisting of two rigidly coupled shafts supported through 4 ball bearings. Only one accelerometer has been used for this purpose at each bearing and the initial results observed are encouraging.

Machine-Learning-Based Mechanical Fault Diagnosis Method

2014 ◽  
Vol 1044-1045 ◽  
pp. 798-800 ◽  
Author(s):  
Hong Zhu
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Science And Technology ◽  
Scientific Research ◽  
Diagnostic Techniques ◽  
Research System ◽  
Intelligent Diagnosis ◽  
Failure Data ◽  
Diagnosis Method ◽  
Diagnosis Model

With the development of science and technology, the theoretical content of mechanical fault diagnosis technology has been initially improved and established a scientific research system. Combining the mechanical diagnostic techniques with the current advanced science and technology, a variety of mechanical fault diagnosis methods have been researched and developed. Mechanical fault diagnosis evolved from empirical diagnosis to mechanical diagnosis and then to the current intelligent learning diagnosis. Now mechanical fault diagnosis collects mechanical failure data precisely mainly by a variety of sensors, uses a variety of fault diagnosis model to conduct diversified and intelligent diagnosis.

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