Multiple Fault Identification Using Vibration Signal Analysis and Artificial Intelligence Methods

2013 ◽  
Vol 430 ◽  
pp. 63-69 ◽  
Author(s):  
Ninoslav Zuber ◽  
Dragan Cvetkovic ◽  
Rusmir Bajrić
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Transmission ◽  
Vibration Signal ◽  
Single Type ◽  
Feature Maps ◽  
Multiple Faults ◽  
Self Organized ◽  
Artificial Neural ◽  
Vibration Signal Analysis

Paper addresses the implementation of feature based artificial neural networks and self-organized feature maps with the vibration analysis for the purpose of automated faults identification in rotating machinery. Unlike most of the research in this field, where a single type of fault has been treated, the research conducted in this paper deals with rotating machines with multiple faults. Combination of different roller elements bearing faults and different gearbox faults is analyzed. Experimental work has been conducted on a specially designed test rig. Frequency and time domain vibration features are used as inputs to fault classifiers. A complete set of proposed vibration features are used as inputs for self-organized feature maps and based on the results they are used as inputs for supervised artificial neural networks. The achieved results show that proposed set of vibration features enables reliable identification of developing bearing and gear faults in geared power transmission systems.

scholarly journals THE IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS IN DESIGNING INTELLIGENT DIAGNOSIS SYSTEMS FOR CENTRIFUGAL MACHINES USING VIBRATION SIGNAL

SINERGI ◽  
2020 ◽  
Vol 25 (1) ◽  
pp. 87
Author(s):  
Dedik Romahadi ◽  
Fajar Anggara ◽  
Andi Firdaus Sudarma ◽  
Hui Xiong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent System ◽  
Vibration Signal ◽  
Training Data ◽  
Accelerometer Sensor ◽  
Vibration Data ◽  
Artificial Neural ◽  
Vibration Signal Analysis ◽  
Short Time

It is important to maintain every machine affecting the process of making sugar to ensure excellent product quality with minimal losses and to accelerate productivity and profitability targets. The centrifuges are widely used in industry today with some being very difficult and critical for surgery, and the collapse of the engine has the ability to cause expensive damage. One of these is the centrifugal machines, and they are expected to be efficient to produce high-quality sugar. Meanwhile, an efficient diagnostic tool to predict the correct time for centrifugal repair is vibration signal analysis namely by attaching the accelerometer sensor to the location of the centrifugal bearing to produce vibration data that is ready to be analyzed. Still, the process requires sufficient insight and experience. The manual method usually used is complicated and requires a lot of time to obtain results of a centrifugal diagnosis. Therefore, this study was conducted to design an intelligent system to diagnose centrifugal vibrations using Artificial Neural Networks (ANN). The situation is involved in applying and training the concept of vibration analysis from spectrum data to ANN to produce diagnostic results according to the spectrum diagnosis reference. The results obtained were quite good with the largest cross-entropy value of 10.67 having 0% error value with the largest Mean Square Error value being 0.0023 while the smallest regression was 0.993. The test conducted on nine new spectrums produced eight true predictions and one false. The system can provide fairly accurate results in a short time. Classification quality improvement can be made by adding training data.

scholarly journals Forecasting cross-border power transmission capacities in Central Western Europe using artificial neural networks

2019 ◽  
Vol 2 (S1) ◽  
Author(s):  
Hazem Abdel-Khalek ◽  
Mirko Schäfer ◽  
Raquel Vásquez ◽  
Jan Frederick Unnewehr ◽  
Anke Weidlich
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Degrees Of Freedom ◽  
Western Europe ◽  
Power Transmission ◽  
Cross Border ◽  
Electricity Trading ◽  
Target Values ◽  
Capacity Calculation ◽  
Artificial Neural

Abstract Flow-based Market Coupling (FBMC) provides welfare gains from cross-border electricity trading by efficiently providing coupling capacity between bidding zones. In the coupled markets of Central Western Europe, common regulations define the FBMC methods, but transmission system operators keep some degrees of freedom in parts of the capacity calculation. Besides, many influencing factors define the flow-based capacity domain, making it difficult to fundamentally model the capacity calculation and to derive reliable forecasts from it. In light of this challenge, the given contribution reports findings from the attempt to model the capacity domain in FBMC by applying Artificial Neural Networks (ANN). As target values, the Maximum Bilateral Exchanges (MAXBEX) have been chosen. Only publicly available data has been used as inputs to make the approach reproducible for any market participant. It is observed that the forecast derived from the ANN yields similar results to a simple carry-forward method for a one-hour forecast, whereas for a longer-term forecast, up to twelve hours ahead, the network outperforms this trivial approach. Nevertheless, the overall low accuracy of the prediction strongly suggests that a more detailed understanding of the structure and evolution of the flow-based capacity domain and its relation to the underlying market and infrastructure characteristics is needed to allow market participants to derive robust forecasts of FMBC parameters.

Automatic Detection of Arrhythmias Using Wavelets and Self-Organized Artificial Neural Networks

2009 ◽  
Author(s):  
Sérgio Renato Rogal Jr ◽  
Alfredo Beckert Neto ◽  
Marcus Vinícius ◽  
Mazega Figueredo ◽  
Emerson Cabrera Paraiso ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Automatic Detection ◽  
Self Organized ◽  
Artificial Neural
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