scholarly journals Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 59 ◽  
Author(s):  
Kenisuomo C. Luwei ◽  
Akilu Yunusa-Kaltungo ◽  
Yusuf A. Sha’aban
Keyword(s):  
Data Fusion ◽  
Condition Monitoring ◽  
Real Life ◽  
General Concept ◽  
Rotating Machines ◽  
Classification Approach ◽  
Vibration Data ◽  
Artificial Neural ◽  
Machine Health ◽  
Artificial Neural Network Ann

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines.

Tool Condition Monitoring Using Artificial Neural Network Models

2022 ◽  
pp. 400-426
Author(s):  
Srinivasa P. Pai ◽  
Nagabhushana T. N.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Medium Carbon Steel ◽  
Tool Condition Monitoring ◽  
Training Time ◽  
Tool Condition ◽  
Cell Structures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

A Novel Approach for Gas Turbine Condition Monitoring Combining CUSUM Technique and Artificial Neural Network

2009 ◽  
Author(s):  
Magnus Fast ◽  
Thomas Palme´ ◽  
Magnus Genrup
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Sequential Analysis ◽  
Ann Model ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Operational Data

Investigation of a novel condition monitoring approach, combining artificial neural network (ANN) with a sequential analysis technique, has been reported in this paper. For this purpose operational data from a Siemens SGT600 gas turbine has been employed for the training of an ANN model. This ANN model is subsequently used for the prediction of performance parameters of the gas turbine. Simulated anomalies are introduced on two different sets of operational data, acquired one year apart, whereupon this data is compared with corresponding ANN predictions. The cumulative sum (CUSUM) technique is used to improve and facilitate the detection of such anomalies in the gas turbine’s performance. The results are promising, displaying fast detection of small changes and detection of changes even for a degraded gas turbine.

scholarly journals Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

2018 ◽  
Vol 211 ◽  
pp. 21005 ◽  
Author(s):  
Kenisuomo C. Luwei ◽  
Jyoti K. Sinha ◽  
Akilu Yunusa-Kaltungo ◽  
Keri Elbhbah
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Critical Speed ◽  
Current Paper ◽  
Rotating Machines ◽  
Test Rig ◽  
Vibration Data ◽  
Identical Machines ◽  
Research Studies

Earlier research studies have suggested the unified vibration-based approach for fault diagnosis (FD) in identical machines with different foundation flexibilities and multi-rotating speeds. Intially the acceleration-based features were used for this approach then further work optimised the approach by combining acceleration and velocity features from vibration data for analysis. However the optimised approach was only tested on the identical machines rotating at different speeds below the machine’s first critical speed. The current paper tends to observe the optimised approach when applied to a test rig operating below and above the machine’s first critical speed.

scholarly journals Application of Artificial Neural Networks for Natural Gas Consumption Forecasting

2020 ◽  
Vol 12 (16) ◽  
pp. 6409 ◽  
Author(s):  
Athanasios Anagnostis ◽  
Elpiniki Papageorgiou ◽  
Dionysis Bochtis
Keyword(s):  
Neural Network ◽  
Natural Gas ◽  
Short Term Memory ◽  
Real Life ◽  
Qualitative Variables ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Network Approaches

The present research study explores three types of neural network approaches for forecasting natural gas consumption in fifteen cities throughout Greece; a simple perceptron artificial neural network (ANN), a state-of-the-art Long Short-Term Memory (LSTM), and the proposed deep neural network (DNN). In this research paper, a DNN implementation is proposed where variables related to social aspects are introduced as inputs. These qualitative factors along with a deeper, more complex architecture are utilized for improving the forecasting ability of the proposed approach. A comparative analysis is conducted between the proposed DNN, the simple ANN, and the advantageous LSTM, with the results offering a deeper understanding the characteristics of Greek cities and the habitual patterns of their residents. The proposed implementation shows efficacy on forecasting daily values of energy consumption for up to four years. For the evaluation of the proposed approach, a real-life dataset for natural gas prediction was used. A detailed discussion is provided on the performance of the implemented approaches, the ANN and the LSTM, that are characterized as particularly accurate and effective in the literature, and the proposed DNN with the inclusion of the qualitative variables that govern human behavior, which outperforms them.

Bio-Inspired Computing through Artificial Neural Network

2017 ◽  
pp. 246-274
Author(s):  
Nilamadhab Dash ◽  
Rojalina Priyadarshini ◽  
Brojo Kishore Mishra ◽  
Rachita Misra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intelligent Systems ◽  
Deductive Reasoning ◽  
Real Life ◽  
Artificial Neural ◽  
Living Organisms ◽  
Artificial Neural Network Ann ◽  
Intelligent Models ◽  
Algorithmic Model

Developing suitable mathematical or algorithmic model to solve real life complex problems is one of the major challenges faced by the researchers especially those involved in the computer science field. To a large extent Computational intelligence has been found to be effective in designing such models. Bio inspired computing is the technique which makes the machines intelligent by adapting the behavior and methods exhibited by the human beings and other living organisms while forming intelligent systems. These intelligent models include the intelligent techniques such as Artificial Neural Network (ANN), evolutionary computation, swarm intelligence, fuzzy system, artificial immune system accompanied by fuzzy logic, expert system, deductive reasoning. All these together form the area of Bio inspired computing. The chapter deals with various bio inspired technique, giving emphasis on issues, development, advances and practical implementations of ANN.

Tool Condition Monitoring Using Artificial Neural Network Models

2020 ◽  
pp. 550-576
Author(s):  
Srinivasa P. Pai ◽  
Nagabhushana T. N.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Medium Carbon Steel ◽  
Tool Condition Monitoring ◽  
Training Time ◽  
Tool Condition ◽  
Cell Structures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

scholarly journals Effective vibration-based condition monitoring (eVCM) of rotating machines

2017 ◽  
Vol 23 (3) ◽  
pp. 279-296 ◽  
Author(s):  
Akilu Yunusa-kaltungo ◽  
Jyoti K. Sinha
Keyword(s):  
Frequency Domain ◽  
Condition Monitoring ◽  
Lessons Learned ◽  
Practical Implementation ◽  
Rotating Machines ◽  
Data Combination ◽  
Content Type ◽  
Vibration Data ◽  
Rotating Machine ◽  
Faults Diagnosis

Purpose The purpose of this paper is mainly to highlight how a simplified and streamlined approach to the condition monitoring (CM) of industrial rotating machines through the application of frequency domain data combination can effectively enhance the eMaintenance framework. Design/methodology/approach The paper commences by providing an overview to the relevance of maintenance excellence within manufacturing industries, with particular emphasis on the roles that rotating machines CM of rotating machines plays. It then proceeds to provide details of the eMaintenance as well as its possible alignment with the introduced concept of effective vibration-based condition monitoring (eVCM) of rotating machines. The subsequent sections of the paper respectively deal with explanations of data combination approaches, experimental setups used to generate vibration data and the theory of eVCM. Findings This paper investigates how a simplified vibration-based rotating machinery faults classification method based on frequency domain data combination can increase the feasibility and practicality of eMaintenance. Research limitations/implications The eVCM approach is based on classifying data acquired under several experimentally simulated conditions on two different machines using combined higher order signal processing parameters so as to reduce CM data requirements. Although the current study was solely based on the application of vibration data acquired from rotating machines, the knowledge exchange platform that currently dominates present day scientific research makes it very likely that the lessons learned from the development of eVCM concept can be easily transferred to other scientific domains that involve continuous CM such as medicine. Practical implications The concept of eMaintenance as a cost-effective and smart means of increasing the autonomy of maintenance activities within industries is rapidly growing in maintenance-related literatures. As viable as the concept appears, the achievement of its optimum objectives and full deployment to the industry is still subjective due to the complexity and data intensiveness of conventional CM practices. In this paper, an eVCM approach is proposed so that rotating machine faults can be effectively detected and classified without the need for repetitive analysis of measured data. Social implications The main strength of eVCM lies in the fact that it permits the sharing of historical vibration data between identical rotating machines irrespective of their foundation structures and speed differences. Since eMaintenance is concerned with driving maintenance excellence, eVCM can potentially contribute towards its optimisation as it cost-effectively streamlines faults diagnosis. This therefore implies that the simplification of vibration-based CM of rotating machines positively impacts the society with regard to the possibility of reducing how much time is actually spent on the accurate detection and classification of faults. Originality/value Although the currently existing body of literature already contains studies that have attempted to show how the combination of measured vibration data from several industrial machines can be used to establish a universal vibration-based faults diagnosis benchmark for incorporation into eMaintenance framework, these studies are limited in the scope of faults, severity and rotational speeds considered. In the current study, the concept of multi-faults, multi-sensor, multi-speed and multi-rotating machine data combination approach using frequency domain data fusion and principal components analysis is presented so that faults diagnosis features for identical rotating machines with different foundations can be shared between industrial plants. Hence, the value of the current study particularly lies in the fact that it significantly highlights a new dimension through which the practical implementation and operation of eMaintenance can be realized using big data management and data combination approaches.

Bio-Inspired Computing through Artificial Neural Network

Fuzzy Systems ◽  
2017 ◽  
pp. 1285-1313
Author(s):  
Nilamadhab Dash ◽  
Rojalina Priyadarshini ◽  
Brojo Kishore Mishra ◽  
Rachita Misra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intelligent Systems ◽  
Deductive Reasoning ◽  
Real Life ◽  
Artificial Neural ◽  
Living Organisms ◽  
Artificial Neural Network Ann ◽  
Intelligent Models ◽  
Algorithmic Model

Developing suitable mathematical or algorithmic model to solve real life complex problems is one of the major challenges faced by the researchers especially those involved in the computer science field. To a large extent Computational intelligence has been found to be effective in designing such models. Bio inspired computing is the technique which makes the machines intelligent by adapting the behavior and methods exhibited by the human beings and other living organisms while forming intelligent systems. These intelligent models include the intelligent techniques such as Artificial Neural Network (ANN), evolutionary computation, swarm intelligence, fuzzy system, artificial immune system accompanied by fuzzy logic, expert system, deductive reasoning. All these together form the area of Bio inspired computing. The chapter deals with various bio inspired technique, giving emphasis on issues, development, advances and practical implementations of ANN.

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