Fault diagnosis of angle grinders and electric impact drills using acoustic signals

Głowacz, Adam; Tadeusiewicz, Ryszard; Legutko, Stanisław; Caesarendra, Wahyu; Irfan, Muhammad; Liu, Hui; Brumerčík, František; Gutten, Miroslav; Sułowicz, Maciej; Daviu, José Alfonso Antonino; Sarkodie-Gyan, Thompson; Frącz, Paweł; Kumar, Anil; Xiang, Jiawei

doi:10.1016/j.apacoust.2021.108070

Cited by 146 publications

(33 citation statements)

References 53 publications

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“…For example, this technique should work well for real-time audio event detection and speech recognition. An important application area related to the ESC-50 dataset for noise identification would be to apply our approach to the more refined problem of fault diagnosis of drilling machines and other machinery [57,58]. Most studies in this area have focused on extracting handcrafted texture features from visual representations of sound [58].…”

Section: Discussionmentioning

confidence: 99%

“…Most studies in this area have focused on extracting handcrafted texture features from visual representations of sound [58]. But recently, there has been some interesting work that has successfully used deep learning to solve this problem [57]. Investigating the performance of our proposed method to tasks such as these would involve combining a different set of network architectures, but the main idea of ensembling different models obtained from many audio augmentation protocols should increase classification performance on these novel tasks and would be worth investigating in the future.…”

Section: Discussionmentioning

confidence: 99%

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An Ensemble of Convolutional Neural Networks for Audio Classification

et al. 2021

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Research in sound classification and recognition is rapidly advancing in the field of pattern recognition. One important area in this field is environmental sound recognition, whether it concerns the identification of endangered species in different habitats or the type of interfering noise in urban environments. Since environmental audio datasets are often limited in size, a robust model able to perform well across different datasets is of strong research interest. In this paper, ensembles of classifiers are combined that exploit six data augmentation techniques and four signal representations for retraining five pre-trained convolutional neural networks (CNNs); these ensembles are tested on three freely available environmental audio benchmark datasets: (i) bird calls, (ii) cat sounds, and (iii) the Environmental Sound Classification (ESC-50) database for identifying sources of noise in environments. To the best of our knowledge, this is the most extensive study investigating ensembles of CNNs for audio classification. The best-performing ensembles are compared and shown to either outperform or perform comparatively to the best methods reported in the literature on these datasets, including on the challenging ESC-50 dataset. We obtained a 97% accuracy on the bird dataset, 90.51% on the cat dataset, and 88.65% on ESC-50 using different approaches. In addition, the same ensemble model trained on the three datasets managed to reach the same results on the bird and cat datasets while losing only 0.1% on ESC-50. Thus, we have managed to create an off-the-shelf ensemble that can be trained on different datasets and reach performances competitive with the state of the art.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

An Ensemble of Convolutional Neural Networks for Audio Classification

et al. 2021

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“…A large number of signal features (parameters) called state indices are used in vibroacoustic diagnostics [ 65 , 66 , 67 , 68 ]. A summary of the parameters determined for the discrete signals used in this paper is presented below.…”

Section: Methodsmentioning

confidence: 99%

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part I: Femoral-Tibial Joint

Karpiński

Krakowski

Jonak

et al. 2022

Sensors

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Osteoarthritis (OA) is a chronic, progressive disease which has over 300 million cases each year. Some of the main symptoms of OA are pain, restriction of joint motion and stiffness of the joint. Early diagnosis and treatment can prolong painless joint function. Vibroarthrography (VAG) is a cheap, reproducible, non-invasive and easy-to-use tool which can be implemented in the diagnostic route. The aim of this study was to establish diagnostic accuracy and to identify the most accurate signal processing method for the detection of OA in knee joints. In this study, we have enrolled a total of 67 patients, 34 in a study group and 33 in a control group. All patients in the study group were referred for surgical treatment due to intraarticular lesions, and the control group consisted of healthy individuals without knee symptoms. Cartilage status was assessed during surgery according to the International Cartilage Repair Society (ICRS) and vibroarthrography was performed one day prior to surgery in the study group. Vibroarthrography was performed in an open and closed kinematic chain for the involved knees in the study and control group. Signals were acquired by two sensors placed on the medial and lateral joint line. Using the neighbourhood component analysis (NCA) algorithm, the selection of optimal signal measures was performed. Classification using artificial neural networks was performed for three variants: I—open kinetic chain, II—closed kinetic chain, and III—open and closed kinetic chain. Vibroarthrography showed high diagnostic accuracy in determining healthy cartilage from cartilage lesions, and the number of repetitions during examination can be reduced only to closed kinematic chain.

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“…Among these advantages are the following: (1) noncontact measuring, which can be useful in harsh and severe environments (e.g., high temperatures and corrosion) [38,39]; (2) cheap and practical technique to deploy compared to vibration-or current-based monitoring [39,40]; (3) machine diagnosis is often preceded by fault-source location by a microphone array. It is then easier to use a few microphones for diagnostic purposes [41].…”

Section: Experimental Test Bench and Datasetmentioning

confidence: 99%

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Habbouche

Benkedjouh

Benbouzid

2021

Entropy

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Failure detection and diagnosis are of crucial importance for the reliable and safe operation of industrial equipment and systems, while gearbox failures are one of the main factors leading to long-term downtime. Condition-based maintenance addresses this issue using several expert systems for early failure diagnosis to avoid unplanned shutdowns. In this context, this paper provides a comparative study of two machine-learning-based approaches for gearbox failure diagnosis. The first uses linear predictive coefficients for signal processing and long short-term memory for learning, while the second is based on mel-frequency cepstral coefficients for signal processing, a convolutional neural network for feature extraction, and long short-term memory for classification. This comparative study proposes an improved predictive method using the early fusion technique of multisource sensing data. Using an experimental dataset, the proposals were tested, and their effectiveness was evaluated considering predictions based on statistical metrics.

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Fault diagnosis of angle grinders and electric impact drills using acoustic signals

Cited by 146 publications

References 53 publications

An Ensemble of Convolutional Neural Networks for Audio Classification

An Ensemble of Convolutional Neural Networks for Audio Classification

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part I: Femoral-Tibial Joint

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

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