Effects of Stress, Repetition, Fatigue and Work Environment on Human Error in Manufacturing Industries

Yeow, Jian Ai; Ng, Poh Kiat; Tan, Khong Sin; Chin, Tee Suan; Lim, Wei Yin

doi:10.3923/jas.2014.3464.3471

Cited by 58 publications

(24 citation statements)

References 18 publications

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“…The stress, the pressure and the fatigue can be divided into 2 families and 4 groups [17]. The workplace and personal factors contributing to employee fatigue [10], [18], as shown in Fig. 3 There are also hidden factors which are quite common to be referred in close statistics but they are not admitted by personal interviews [19]: Work demands; Hierarchy pressure; Time pressure and economic pressure.…”

Section: The Need For Regulationsmentioning

confidence: 99%

Stress, Pressure and Fatigue on Aircraft Maintenance Personal

Santos

Melício

2019

IREASE

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This paper focuses on an analysis of the stress, the pressure and the fatigue as part of the Dirty Dozen and Human Factors procedures. An online international survey has been carried out to ascertain the professional levels of the fatigue, the stress and their pressure exposure. This work is a contribution to the aeronautical safety in order to alert authorities about the stress, the pressure and the fatigue that aircraft maintenance personnel suffers. Safety is the main driver in aviation related professions. Maintenance related personnel is constantly subjected to several external circumstances that might originate errors in the performance or evaluation in maintenance related tasks. Authorities have clearly regulated flight crew and air traffic controllers working and resting periods, but maintenance personnel regulations do not reflect the same procedures. The Aeronautical industry 4.0 with the upcoming digital transformation will increase the safety margin and it will reduce the aircraft maintenance ground time.

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Section: The Need For Regulationsmentioning

confidence: 99%

Stress, Pressure and Fatigue on Aircraft Maintenance Personal

Santos

Melício

2019

IREASE

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“…The authors proposing the OCRA checklist suggested its use only for an initial screening of the workspace to examine [11]. The computation of OCRA is performed in several industrial settings, such as manufacturing [12], large-scale retail stores [13], assembly [14,15], ergonomics [16], automotive [17] and production processes [18].…”

Section: Introductionmentioning

confidence: 99%

On the OCRA Measurement: Automatic Computation of the Dynamic Technical Action Frequency Factor

Taborri

Bordignon²,

Marcolin³

et al. 2020

Sensors

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OCRA (OCcupational Repetitive Action) is currently one of the most widespread procedures for assessing biomechanical risks related to upper limb repetitive movements. Frequency factor of the technical actions represents one of the OCRA elements. Actually, the frequency factor computation is based on workcycle video analysis, which is time-consuming and may lead to up to 30% of intra-operator variability. This paper aims at proposing an innovative procedure for the automatic counting of dynamic technical actions on the basis of inertial data. More specifically, a threshold-based algorithm was tested in four industrial case studies, involving a cohort of 20 workers. Nine combinations of the algorithm were tested by varying threshold values related to time and amplitude. The computation of frequency factor showed an average relative error lower than 5.7% in all industrial-based case studies after the appropriate selection of the time and amplitude threshold values. These findings open the possibility to use the threshold-based algorithm proposed here for the automatic computation of OCRA frequency factor, avoiding the time efforts in video analysis.

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“…Another disadvantage of manual inspection is related to human factors of the operator. In inspection task which require monotonous and repeating activities, the occurrence of human error would likely to increase due to fatigue and repetition [9]. Furthermore, The working environment of tiles inspection can be found to be hazardous and unhealthy which may also contributes significantly to human error [9].…”

Section: Introductionmentioning

confidence: 99%

Multi-class Classification of Ceramic Tile Surface Quality using Artificial Neural Network and Principal Component Analysis

Ramadhan¹,

Rachmat

Atmaja

et al. 2019

Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018)

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The visual inspection of ceramic tile surface is an important factor which may influence the perceived surface quality of the product. While manual labor offers an alternative in the task of visual inspection, human limitation related problem such as fatigue and safety may pose an undesirable inspection performance when applied in mass production industry. This study attempted to automate the process of ceramic quality inspection through computerized image classification. Specifically, a dimensionality reduction technique called Principal Component Analysis and classification technique Artificial Neural Network were incorporated in the study to classify five categories of surface quality: normal, crack, chip-off, scratch and dry spots. Given 400 principal components as the input layer and three hidden layers consisting 150 hidden units each, the model was trained under 19,696 training images by using Adam Optimization. By performing prediction on the test set consisting of 4,256 images, the trained model was able to achieve the classification accuracy of 90.13%.

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Effects of Stress, Repetition, Fatigue and Work Environment on Human Error in Manufacturing Industries

Cited by 58 publications

References 18 publications

Stress, Pressure and Fatigue on Aircraft Maintenance Personal

Stress, Pressure and Fatigue on Aircraft Maintenance Personal

On the OCRA Measurement: Automatic Computation of the Dynamic Technical Action Frequency Factor

Multi-class Classification of Ceramic Tile Surface Quality using Artificial Neural Network and Principal Component Analysis

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