An Intelligence-Based Model for Condition Monitoring Using Artificial Neural Networks

This paper reports a newly developed Condition-Based Maintenance (CBM) model based on Artificial Neural Networks (ANNs) which takes into account a feature (e.g., vibration signals) from a machine to classify the condition into normal or abnormal. The model can reduce equipment downtime, production loss, and maintenance cost based on a change in equipment condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, debris content, and volume of material). The model can effectively determine the maintenance/service time that leads to a low maintenance cost in comparison to other types of maintenance strategy. Neural Networks tool (NNTool) in Matlab is used to apply the model and an illustrative example is discussed.

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Application of artificial neural networks and principal component analysis on vibration signals for automated fault classification of roller element bearings

Eksploatacja i Niezawodnosc - Maintenance and Reliability ◽

10.17531/ein.2016.2.19 ◽

2016 ◽

Vol 18 (2) ◽

pp. 299-306 ◽

Cited By ~ 8

Author(s):

Ninoslav Zuber ◽

Rusmir BAJRIĆ

Keyword(s):

Neural Networks ◽

Principal Component Analysis ◽

Artificial Neural Networks ◽

Principal Component ◽

Component Analysis ◽

Fault Classification ◽

Vibration Signals ◽

Artificial Neural

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Intelligent Monitoring Equipment in Power Plants using IEC61850 Sample Values, Paraconsistent Artificial Neural Networks and Condition Based Maintenance

10.20906/cps/cob-2015-0294 ◽

2015 ◽

Author(s):

Antonio Bernardo de Vasconcellos Praxedes ◽

Alberto José Alvares

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Power Plants ◽

Condition Based Maintenance ◽

Monitoring Equipment ◽

Intelligent Monitoring ◽

Artificial Neural

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Anomaly detection in energy consumption for Condition-Based maintenance of Compressed Air Generation systems: an approach based on artificial neural networks

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2018.08.439 ◽

2018 ◽

Vol 51 (11) ◽

pp. 1131-1136 ◽

Cited By ~ 5

Author(s):

A. Santolamazza ◽

V. Cesarotti ◽

V. Introna

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Energy Consumption ◽

Anomaly Detection ◽

Condition Based Maintenance ◽

Compressed Air ◽

Artificial Neural

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Reliability and Maintenance Cost Forecasting for Systems with Multistate Components Using Artificial Neural Networks

2019 4th International Conference on System Reliability and Safety (ICSRS) ◽

10.1109/icsrs48664.2019.8987700 ◽

2019 ◽

Author(s):

Phuc Do ◽

Benoit Iung ◽

Cristiano Cavalcante

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Maintenance Cost ◽

Artificial Neural ◽

Cost Forecasting

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ART-2 Artificial Neural Networks Applications for Classification of Vibration Signals and Operational States of Wind Turbines for Intelligent Monitoring

Lecture Notes in Mechanical Engineering - Advances in Condition Monitoring of Machinery in Non-Stationary Operations ◽

10.1007/978-3-642-39348-8_58 ◽

2013 ◽

pp. 679-688 ◽

Cited By ~ 4

Author(s):

Tomasz Barszcz ◽

Andrzej Bielecki ◽

Mateusz Wójcik ◽

Marzena Bielecka

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wind Turbines ◽

Intelligent Monitoring ◽

Vibration Signals ◽

Artificial Neural

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Parameter identification for fractional-fractal model of filtration-consolidation dynamics using artificial neural networks

Physico-mathematical modelling and informational technologies ◽

10.15407/fmmit2021.32.052 ◽

2021 ◽

pp. 52-57

Author(s):

Vsevolod Bohaienko ◽

Volodymy y Bulavatsky ◽

Anatolij Gladky

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Fractional Derivatives ◽

Identification Problem ◽

Fractal Model ◽

Fixed Number ◽

Data Sets ◽

Noise Levels ◽

Filtration Consolidation ◽

Artificial Neural

Artificial neural networks are applied to solve parameters identification problem for one-dimensional fractional-fractal model of filtration consolidation processes in geo-porous media in the conditions of salt transfer. Based on the indicators of the state of the process in a fixed number of observation points, the values of the orders of fractional derivatives with respect to time and space variables were restored. Testing results based on data sets obtained from noised solutions of the direct problem show the adequacy of fractional derivatives orders restoration with at least 25 observation points and noise levels less than 10%.

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Intelligence Monitor and Diagnosis to High Speed Brushes Aeration Mechanics Based on Turbulent Flow Displacement Sensing Theory

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.426-427.191 ◽

2010 ◽

Vol 426-427 ◽

pp. 191-196

Author(s):

H.I. Liu ◽

X.P. Li ◽

Yan Nian Rui ◽

Ying Ping He

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Eddy Current ◽

High Speed ◽

Current Effect ◽

Vibration Signals ◽

Flow Displacement ◽

Eddy Current Effect ◽

Artificial Neural ◽

Main Spindle

High Speed Brushes Aeration Mechanics are the effective aeration equipments which are widely used in the environmental protection. Because of the big span of main spindle and its high speed when it is working, the breakdown sometimes occurs. It is very importance to monitor its condition and diagnose its breakdowns. Turbulent Flow Displacement Sensors are the non-contact types which are based on eddy current effect. It has many advantages, such as good linearity, wide frequency response scope, convenience installment and so on. So it is very suitable for the main spindle’s vibration signals of a high speed brushes aeration mechanic are monitored. With the development of Artificial Neural Networks technology, the equipment breakdown diagnosis has realized intellectualization. The recognition of equipment failure types is one of the most important studying domains of Artificial Neural Networks at present. Based on the research of eddy current effect and Artificial Neural Networks, we build up a test system which can monitor condition and diagnose breakdown to a GSB-12 high speed brushes aeration mechanic. With the help of it, the vibration signals of the measurement points on the main spindle are measured at two mutually vertical positions. The signals’ base frequency and multiplicative frequency are taken as characteristic value. Six common breakdowns are selected and to be taken as the standard sample and there are 3 lays in the neural network. Using FBP algorithm, we get a satisfied effect. The experiment has confirmed that this method is advanced, reliable and practical. It provides a new method about intelligent monitor and breakdown diagnosis to high speed brushes aeration mechanics’ condition.

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