Fan Fault Diagnosis Using Acoustic Emission and Deep Learning Methods

Ciaburro, Giuseppe; Padmanabhan, Sankar; Maleh, Yassine; Puyana‐Romero, Virginia

doi:10.3390/informatics10010024

Cited by 10 publications

(6 citation statements)

References 60 publications

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“…Ciaburro et al [129] investigated a CNN-based method in order to identify fan faults using the acoustic emissions caused by the dust deposits on the blades of an axial fan. The acoustic emission was monitored both in its mechanical and aerodynamic noises; the latter remarkably contributed to the total noise.…”

Section: Fault Detectionmentioning

confidence: 99%

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

Villano,

Mauro,

Pedace

2024

Thermo

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Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems.

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Section: Fault Detectionmentioning

confidence: 99%

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

Villano,

Mauro,

Pedace

2024

Thermo

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“…The proposed approach can detect the bearing fault correctly. A new methodology for ventilator acoustic fault diagnosis was developed [12]. Two states of the ventilator were analyzed: fault, and no-fault.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Fault diagnosis of electrical faults of three phase induction motors using acoustic analysis

Lucas,

Lui,

Brumercik

et al. 2024

Bulletin of the Polish Academy of Sciences Technical Sciences

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Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: Rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with twobroken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using Fast Fourier Transform(FFT), a new feature extraction method called MoD-7 (Maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications.

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“…To understand the effect of various condition just usage of online condition monitoring may not be sufficient and to its widespread usage ML methods [43] now are required. Ciaburro et al [44] proposed a new methodology for automating the fan maintenance procedures based on the recording of the acoustic emission. The failure diagnosis using deep learning was evaluated for the detection of dust deposits on the blades of an axial fan.…”

Section: Researchmentioning

confidence: 99%

Online Gear Wear Particle Detection and Categorization Using a Convolutional Neural Network Algorithm Integrated with Cascade Classifier

Gupta¹,

Hirani²,

Muzakkir³

2023

Tribol. Ind.

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Most gears fail because of wear caused by rubbing, metal to metal contact, contamination, or breakdown of lubrication. Because of this, figuring out how to find and sort wear debris particles is an important area of research for both predictive and proactive maintenance. By putting these wear particles into different categories like spherical, cutting, fatigue, sliding and rubbing, it would be possible to identify the wear modes present in the gearbox and predict the nature of failure and condition of the system. The present research aims to automate the detection and classification process using the Convolutional Neural Network (CNN) integrated with Cascade classifier. CNN automatically extracts different suitable features from images by applying multiple filters on it and also reduces the complexity of image processing whereas the Cascade classifier is used to detect the particles by differentiating between positive and negative images by applying the Haar-like features into it. The objective of the research work is to provide a most efficient and accurate detection and classification of wear debris particles using a trained cascade classifier integrated with a customized lightweight CNN model named as Wear Particle Classifier Net (WPCnet).

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Fan Fault Diagnosis Using Acoustic Emission and Deep Learning Methods

Cited by 10 publications

References 60 publications

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

Fault diagnosis of electrical faults of three phase induction motors using acoustic analysis

Online Gear Wear Particle Detection and Categorization Using a Convolutional Neural Network Algorithm Integrated with Cascade Classifier

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