End-of-line inspection for annoying noises in automobiles: Trends and perspectives

Cook, Valerie G. Caryer; Ali, Ahad

doi:10.1016/j.apacoust.2011.06.019

Cited by 21 publications

(8 citation statements)

References 21 publications

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“…In [168] Jiang pone l'attenzione sullo studio acustico del solo condotto di scarico per identificare eventuale anomalie di combustione in motori diesel. In [169] vengono illustrati approcci per valutare l'esistenza di rumori fastidiosi in interno abitacolo di un veicolo intero al termine della linea di produzione attraverso tecniche che utilizzano mappe spaziali quali Beamforming e Near Field Acoustic Holography.…”

Section: Acoustic Signal-based Techniquesunclassified

Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques

Delvecchio

Bonfiglio

Pompoli

2018

Mechanical Systems and Signal Processing

150

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Section: Acoustic Signal-based Techniquesunclassified

Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques

Delvecchio

Bonfiglio

Pompoli

2018

Mechanical Systems and Signal Processing

150

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“…The acoustic signals were collected near a fixture, that vibrates the products to resemble the driving situation. The dominant sound quality metrics in a multiple linear regression equation was applied for inspection algorithm [23].…”

Section: Inspection Using Microphonementioning

confidence: 99%

“…The noises are caused by a lot of potential sources, for instance, error in assembly operations, defects in components, and dimensional control issues [22]. Acquisition of the annoying noise has been conducted by the microphones [23]. However, the shop floor noises are known to easily contaminate the signals of interest as mentioned in section 2.5.…”

mentioning

confidence: 99%

In-Process Noise Detection System for Product Inspection by Using Acoustic Data

Baek

Kim

2019

IFIP Advances in Information and Communication Technology

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Objective quality inspection of products in manufacturing process is inseparable from sensor technologies. Inspection methods using analysis of vibration signals have advantages such as being non-destructive, accurate, fast for in-process application. This paper presents recent developments and applications of in-process product inspection which use vibration and acoustic data in various industry. In detail, the inspection system developed with accelerometer, laser vibrometer, laser ultrasonic sensor, acoustic emission sensor, and microphone are presented. An in-process noise detection system for car body parts inspection is introduced as a case study.

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“…25 Cook, et al have elaborated on trends and perspectives of the end of line inspection in a technical note and proposed that the ability of psychoacoustic features to objectively inspect the devices can overcome the limitations of human auditors. 26 Hence, attempts are made to develop a technique to identify faults using psychoacoustic features and artificial intelligence technique to help the operator to objectively make a decision about the acceptability of the gearbox. Kane and Andhare have reported the use of psychoacoustics for a single fault, i.e.…”

Section: Introductionmentioning

confidence: 99%

End of the Assembly Line Gearbox Fault Inspection Using Artificial Neural Network and Support Vector Machines

Kane¹,

Andhare²

2019

The International Journal of Acoustics and Vibration

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Gear fault diagnosis is important not only during the routine maintenance of machinery, but also during the inspection of newly manufactured gearboxes at the end of the assembly line. This paper discusses the application of an artificial neural network (ANN) and a support vector machine (SVM) for identifying faults in the gearbox, using the psychoacoustic and conventional statistical features extracted from acoustics and vibration signals. It is observed that at the end of the assembly line, the gearbox is tested by mounting it on a test bench and driving it by an electric motor. Based on the sound emitted while running on the test bench, the operator decides on the acceptance of the gearbox for further assembly on a vehicle or machine. This method of acceptance or rejection of the gearbox involves subjectivity and it is not reliable. Hence, it is important to have a reliable and objective fault detection and diagnosis method. To eliminate subjectivity, psychoacoustic features, which are derived from the science of listening in human beings, are proposed to be used as features, along with ANN and SVMs as classifiers. To ascertain the ability of the psychoacoustic features to classify faults, laboratory experiments are carried on a test setup by simulating faults like a gear shaft misalignment, a profile error of a gear tooth, a crack at the root of the tooth, and a broken tooth. ANN and SVM are trained with the psychoacoustic features extracted from the acoustic signal and other statistical features from the acoustics and vibration signals. The trained SVM and ANN are tested for fault classification for these features and their accuracy is compared. Fault classification accuracy is found to be 95.65% for ANN and 93.44% for SVM with psychoacoustic features and is found to be better than pure statistical features obtained from the vibration and acoustic signals. With the optimised ANN and SVM architecture, SVM is found to be performing better than ANN. It is concluded that the psychoacoustic features, along with the ANN and SVM method, could be adopted at the end of assembly line inspection to make the inspection process more objective.

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End-of-line inspection for annoying noises in automobiles: Trends and perspectives

Cited by 21 publications

References 21 publications

Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques

Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques

In-Process Noise Detection System for Product Inspection by Using Acoustic Data

End of the Assembly Line Gearbox Fault Inspection Using Artificial Neural Network and Support Vector Machines

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