Fault diagnosis in a distributed motor network using Artificial Neural Network

Altaf, Saud; Al-Anbuky, Adnan; GholamHosseini, Hamid

doi:10.1109/speedam.2014.6871946

Cited by 12 publications

(16 citation statements)

References 11 publications

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“…While Feed Forward Neural Network (FFNN) technique has been allowing input/output mapping that will be employed between the nodes with nonlinear relationship [19], neural network has ability to recognize the abnormal representation of electrical signal due to their inherent capacity of classification and generalization process, especially, when response time and sensitivity of the actual process presented the repetition of fault sets and created the uncertainty in fault identification in power network. Several training algorithms have been proposed, but BPNN algorithm is most commonly used in for isolated machine faults [20]. A multilayer FFNN is used in this research for type identification and localization of fault within network.…”

Section: Neural Network Architecture Modelling To Avoid Uncertainty Mmentioning

confidence: 99%

Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

Altaf

Soomro

Mehmood³

2017

Modelling and Simulation in Engineering

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In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature Analysis (MCSA) method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.

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Section: Neural Network Architecture Modelling To Avoid Uncertainty Mmentioning

confidence: 99%

Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

Altaf

Soomro

Mehmood³

2017

Modelling and Simulation in Engineering

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“…Therefore, it is imperative that the level of asymmetry is calculated in an efficient and effective manner. There are some drawbacks and problems associated with the use of conventional digital protective relays which are listed as follows [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]:…”

Section: Introductionmentioning

confidence: 99%

“…In the past decade, many condition monitoring and fault diagnosis methods for generators, including signal-based, model-based and data-driven, were investigated [13][14]. When generators are connected in power networks, current/voltage signature signals of faulty electric machines will propagate through the power networks [15]. The generated electrical waveforms (e.g.…”

Section: Introductionmentioning

confidence: 99%

Detection and assessment scheme of voltage and current unbalance for three phase synchronous generators using dual numerical techniques based on correlation and deviation percentage concepts

Mahmoud

2021

The Journal of Engineering

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A differentiation in three-phase voltages or currents is termed as unbalance. Unbalance can be caused by unbalanced loads and power system faults. This disturbance results in serious problems in system elements, particularly to synchronous generators, due to the deviation of magnitude, frequency or phase shift. The ability to detect unbalance as well as to identify the severity level of unbalance is very important in power grids. This paper describes a protection scheme for voltage and current unbalance in synchronous generators. The scheme is based on dual numerical techniques application utilizing correlation and deviations percentages for voltage and current signals to detect and assess the unbalance situations. Different types of disturbances are simulated to verify the effectiveness of the proposed scheme using ATP and MATLAB programs. The simulation results prove that the scheme is successful for unbalance detection and assessment of synchronous generators. The different faults and operating conditions have no effect on the proposed scheme reliability. Also, the results show that the proposed scheme is able to classify the unbalance into different severity levels. Besides the obtained results reveal this scheme is effective and secure to the acceptable overloading and temporary faults in power systems.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Ethylene can greatly affect the value of harvested fruit produce. It can be advantageous or deleterious depending on the product, its ripening stage, and its desired use [ 10 ]. Ethylene production is greatly affected by the storage temperature of produce, and ethylene production is generally reduced at low temperatures.…”

Section: Introductionmentioning

confidence: 99%

“…1-MCP is a well-known ethylene antagonist that suppresses ethylene action by blocking ethylene receptor sites [ 13 ]. The alternate of 1-MCP for the ethylene receptor is about ten times better than that of ethylene [ 10 ]. There are many papers on the proficiency of 1-MCP ethylene antagonist on constraining the effects of ethylene on the green life of bananas, and 1-MCP concentration mixtures, temperature, and duration of treatment have been under investigation [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique

Altaf

Ahmad

Zaindin

et al. 2020

Sensors

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Real-time monitoring of fruit ripeness in storage and during logistics allows traders to minimize the chances of financial losses and maximize the quality of the fruit during storage through accurate prediction of the present condition of fruits. In Pakistan, banana production faces different difficulties from production, post-harvest management, and trade marketing due to atmosphere and mismanagement in storage containers. In recent research development, Wireless Sensor Networks (WSNs) are progressively under investigation in the field of fruit ripening due to their remote monitoring capability. Focused on fruit ripening monitoring, this paper demonstrates an Xbee-based wireless sensor nodes network. The role of the network architecture of the Xbee sensor node and sink end-node is discussed in detail regarding their ability to monitor the condition of all the required diagnosis parameters and stages of banana ripening. Furthermore, different features are extracted using the gas sensor, which is based on diverse values. These features are utilized for training in the Artificial Neural Network (ANN) through the Back Propagation (BP) algorithm for further data validation. The experimental results demonstrate that the projected WSN architecture can identify the banana condition in the storage area. The proposed Neural Network (NN) architectural design works well with selecting the feature data sets. It seems that the experimental and simulation outcomes and accuracy in banana ripening condition monitoring in the given feature vectors is attained and acceptable, through the classification performance, to make a better decision for effective monitoring of current fruit condition.

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Fault diagnosis in a distributed motor network using Artificial Neural Network

Cited by 12 publications

References 11 publications

Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

Detection and assessment scheme of voltage and current unbalance for three phase synchronous generators using dual numerical techniques based on correlation and deviation percentage concepts

Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique

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