Background noise mitigation of dual microphone system for defect detection in electrical cable connection

Joshi, Neha S.; Singh, Sonali; Krugh, Matthew; Mears, Laine

doi:10.1016/j.promfg.2018.07.139

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…The direct analysis of acoustic signals is difficult because these signals are easily contaminated by background noise. This necessitates the use of a noise reduction procedure, which utilizes subtractive noise reduction after estimation of the background noise [19]. However, because the microphones installed inside the workstation record all types of noise, it is impossible to estimate the exact background noise by using the signal-processing methods.…”

Section: Proposed In-process Bsr Noise Detection Systemmentioning

confidence: 99%

An in-Process Inspection System to Detect Noise Originating from within the Interior Trim Panels of Car Doors

Baek

Kim

2020

Sensors

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Car body parts are sometimes responsible for irritating noise caused by assembly defects. Typically, various types of noise are known to originate from within the interior trim panels of car doors. This noise is considered to be an important factor that degrades the emotional satisfaction of the driver of the car. This research suggests an in-process inspection system consisting of an inspection workstation and a noise detection method. The inspection workstation presses down the car door trim panel by using a pneumatic pusher while microphones record the acoustic signals directly above the door trim panel and on the four sides of the workstation. The collected signals are analyzed by the proposed noise detection method after applying noise reduction. The noise detection method determines the presence of irritating noise by using noise source localization in combination with the time difference of arrival method and the relative signal strengths. The performance of the in-process noise detection system was evaluated by conducting experiments on faulty and healthy car door trim panels.

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Section: Proposed In-process Bsr Noise Detection Systemmentioning

confidence: 99%

An in-Process Inspection System to Detect Noise Originating from within the Interior Trim Panels of Car Doors

Baek

Kim

2020

Sensors

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“…A robotic wiring harness assembly system was developed to perform the mating process of electronic connectors Robotic connector assembly Joshi et al (2018) Studied to separate the "clicking" sound of mating electrical connectors from background noise using spectral subtraction in two different noise levels, i.e., semianechoic chamber as 30 dB and then noisy environment as 87 dB Detection of non-OK connector assembly by "click sound"…”

Section: Chen Et Al (2014)mentioning

confidence: 99%

“…In manufacturing plants, the noise level can be as high as 125 dB for short periods of time (Liu and Liptak, 1997). Joshi et al (2018) studied to separate the "clicking" sound of connectors from background noise using spectral subtraction in two different noise levels, i.e. semi-anechoic chamber as 30 dB and then noisy environment as 87 dB, to quantify the ability to detect and classify connection failures.…”

Section: Introductionmentioning

confidence: 99%

Failure prediction in electrical connector assembly: a case in automotive assembly process

Altinisik

Yildirim

2020

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Purpose Electrical defects cover an important part of assembly defects and strongly affect the vehicle system performance. Almost 40% of assembly defects are classified as human errors and electrical connection failures represent a significant part of them. Humans still remain a cost-effective solution for the flexible manufacturing systems with increasing product complexity. So, understanding human behaviors is still a challenging task. The purpose of this study is to define, prioritize and validate the critical factors for the complexity of electrical connector plugin process. Design/methodology/approach The critical variables were defined by the expert team members. The required number of measurements and variables were revised resulting preliminary analysis of binary logistic regression. After the revision of measurement plan, the list of critical input variables and the mathematical model were defined. The model has been validated by the fitted values of the residuals (FITS analysis). Findings To the best of the authors’ knowledge, this is one of the limited studies, which defines the critical factors for electrical connection process complexity. Female connector harness length, connector width/height/length differences, operator sense of correct connector matching and ergonomy were defined as the factors with the highest impact on the failure occurrence. The obtained regression equation strongly correlates the failure probability. Practical implications The obtained mathematical model can be used in new model development processes both for the product and assembly process design (ergonomy, accessibility and lay-out). Originality/value The obtained risk factors demonstrated a strong correlation with assembly process complexity and failure rates. The output of this study would be used as an important guide for process (assembly line ergonomy, accessibility and lay-out) and product design in new model development and assembly ramp-up phases.

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“…5G wireless technology can be utilized to reduce communication latency, maximizes throughput, increases interconnectivity, and potentially speeds up the computational tasks by enabling distributed computing functionalities. Security and privacy are the main challenges in the realization of distributed clouds which could be addressed through the adaptation of blockchain technology [21,25,26] and finally, industrial AI facilitates autonomous and distributed decision making. Moreover, in the proposed architecture, multi-cloud providers work together and may share their infrastructure in a P2P interconnected network to enhance resource availability and reduce operational expenses.…”

Section: The Proposed Edge-fog-cloud Architecture In 5g Environmentmentioning

confidence: 99%

“…Such network supports mobile applications, easy deployment, high scalability, low latency, and geopolitical independence at a lower cost and restrictions than traditional cloud computing structures. Micro clouds could be essential for delay-sensitive manufacturing applications like real-time monitoring systems [26,27], smart assembly platforms [27], and human-machine integrated data representation [28]. A machine-level Micro-Cloud manages six major aspects (6M) namely, 1-Material: property, strengths, and functions 2-Machine: precision, calibration, and automation 3-Methods: tools, analytics, and knowledge, 4-Measurement: calibration, noise reduction, accuracy, 5-Maintenance: monitor, predict, and avoid 6-Models: predict, optimize, and resilient.…”

Section: Edge Micro-cloudsmentioning

confidence: 99%

From Centralized Systems to Distributed 5G-Enabled Manufacturing

Baxter¹,

Lundberg²

2020

Preprint

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The landscape of centralized cloud computing is now changing to distributed and decentralized clouds with promising impacts on energy consumption, resource availability, resilience, and customer experience. This research highlights the impacts of emerging IT trends, namely, 5G wireless technology, blockchain, and industrial Artificial Intelligence (AI) in development and realization of the next generation of cloud computing. Integration of these technologies in cyber-physical system and cloud manufacturing paradigms is explained and a unified edge-fog-cloud architecture is proposed for successful implementation in manufacturing systems.

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Background noise mitigation of dual microphone system for defect detection in electrical cable connection

Cited by 7 publications

References 9 publications

An in-Process Inspection System to Detect Noise Originating from within the Interior Trim Panels of Car Doors

An in-Process Inspection System to Detect Noise Originating from within the Interior Trim Panels of Car Doors

Failure prediction in electrical connector assembly: a case in automotive assembly process

From Centralized Systems to Distributed 5G-Enabled Manufacturing

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