Lean Production and Its Impact on Worker Health

Lean is currently applied successfully in many industrial sectors. However, its value and impact on human health is not fully understood. To gain a better understanding, this chapter explores how ergonomics force and fatigue evaluation methods can be applied in a manner to enhance lean initiatives. These methods incorporate ergonomic-related variables of force type, force duration, force frequency, and degree of awkward posture, and incorporate the recommended cumulative rest allowance (RCRA) model as a practical fatigue-based metric. These methods and their application are discussed.

Evaluation of Structural Behavior and Fatigue Performance of a KR-Type Rail Clip

Rail clips are essential components of rail fastening systems that clamp the rails to sleepers. Fatigue damage of rail clips has been recently reported in railway lines. However, there has been a lack of research investigating this fatigue issue. The KR-type rail fastening system has been recently developed and used in some domestic railways. This study aimed at evaluating the structural behavior and fatigue performance of the KR-type rail clip. The assembly test performed in the laboratory showed that the stresses induced in the rail clips after tightening, particularly at the stress concentration locations, exceeded the yield stress, indicating that the rail clip could be vulnerable to fatigue cracking when combined with the stress range during repeated trainloads. The finite element analysis results, which revealed a good correlation with the experiments, were used to evaluate the fatigue performance of the rail clip by adopting the modified Goodman fatigue criteria. The fatigue evaluation results indicated that when the vertical rail displacement during train operation exceeded 2 mm, the rail clips could potentially suffer from fatigue failure.

Investigation of the Fatigue Stress of Orthotropic Steel Decks Based on an Arch Bridge with the Application of the Arlequin Method

Orthotropic steel decks are widely used in the construction of steel bridges. Although there are many fatigue-evaluation methods stipulated by codes, unexpected fatigue cracks are still detected in some bridges. To justify whether the local finite element model commonly used in fatigue investigations on orthotropic decks can correctly instruct engineering practices, the Arlequin framework is applied in this paper to determine the full fatigue stress under traffic loads. The convergence on and validity of this application for orthotropic decks are checked. Results show that the Arlequin model for deck-fatigue analysis established in this paper tends to be an efficient method for complete fatigue stress acquisition, whereby the vulnerable sites of orthotropic steel decks under traffic loads are defined. Vehicles near the flexible components, such as hangers or cables, can have adverse effects on the fatigue durability of decks. Additionally, the total number of vehicles and their arrangement concentration also affect fatigue performance. Complex traffic conditions cannot be fully loaded in local models. Regardless of the gross bridge mechanics and deck deformation, the fatigue stress range is underestimated by about 30–40%. Such a difference in fatigue assessment seems to explain the premature cracks observed in orthotropic steel decks.

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Brain fatigue is often associated with inattention, mental retardation, prolonged reaction time, decreased work efficiency, increased error rate, and other problems. In addition to the accumulation of fatigue, brain fatigue has become one of the important factors that harm our mental health. Therefore, it is of great significance to explore the practical and accurate brain fatigue detection method, especially for quantitative brain fatigue evaluation. In this study, a biomedical signal of ballistocardiogram (BCG), which does not require direct contact with human body, was collected by optical fiber sensor cushion during the whole process of cognitive tasks for 20 subjects. The heart rate variability (HRV) was calculated based on BCG signal. Machine learning classification model was built based on random forest to quantify and recognize brain fatigue. The results showed that: Firstly, the heart rate obtained from BCG signal was consistent with the result displayed by the medical equipment, and the absolute difference was less than 3 beats/min, and the mean error is 1.30 ± 0.81 beats/min; secondly, the random forest classifier for brain fatigue evaluation based on HRV can effectively identify the state of brain fatigue, with an accuracy rate of 96.54%; finally, the correlation between HRV and the accuracy was analyzed, and the correlation coefficient was as high as 0.98, which indicates that the accuracy can be used as an indicator for quantitative brain fatigue evaluation during the whole task. The results suggested that the brain fatigue quantification evaluation method based on the optical fiber sensor cushion and machine learning can carry out real-time brain fatigue detection on the human brain without disturbance, reduce the risk of human accidents in human–machine interaction systems, and improve mental health among the office and driving personnel.

Fatigue Evaluation of Offshore Steel Structures Considering Stress Concentration Factor

The installation of offshore structures and facilities in the marine environment, usually for the production and transmission of oil, gas exploration, electricity, and other natural resources is referred to as offshore construction. Since offshore structures are subjected to changing threats to the environment year-round. Fatigue behavior prediction noticed on these structures should be considered during the design stage. Fatigue is one of the failure mechanisms of offshore steel structures, and it must be investigated properly during system design. The fatigue analysis of offshore structures under drag wave force, total wave force, total moment about the sea bed, and other variables are reviewed thoroughly. The structure's dynamic response becomes a critical aspect in the whole design process. The fatigue analysis was carried out using MATLAB software, material properties of the offshore structure, and wave spectrum characteristics in this study. This study shows the JONSWAP spectrum and stress concentration analysis prediction. The offshore support structure that is predicted during the design phase will help to keep the stress concentration factor below the fatigue threshold and anticipate safe life design, according to the results of the fatigue study. The fatigue performances of tripod and jacket steel support structures in intermediate waters depth are compared in this project (20-50 m). The North Atlantic Ocean is used as a reference site, with a sea depth of 45 meters. The tripod and jacket support structures will be designed by using current industry standards. Keywords: [Fatigue evaluation, North Atlantic Ocean and Failure].

Human Fatigue Evaluation by Face's Image Analysis Based upon Convolutional Neural Networks

This article presents solutions to the person's fatigue recognition problem by the face's image analysis based on convolutional neural networks. In the present paper, existing algorithms were considered. A new model's architecture was proposed and implemented. Resultant metrics of the model were evaluated.

Subsea Tree Fatigue Mitigation Solutions For Shallow Water Drilling

The offshore drilling industry is advancing technologies to extend deep water drilling technologies and attain feasibility of operations at deeper depths and higher pressures. However, shallow water operations themselves pose a certain unique set of challenges that need to be addressed with customized and innovative solutions. While shallow water poses certain benefits and conveniences to the operations, like ease of retrieval and better access to wells, there are significant challenges in terms of operational down time caused by limited operability and poor drilling riser and subsea hardware fatigue performance. Shallow water operations do not have the advantage of deep water drilling where the motions and loads imparted to the subsea blowout preventer (BOP) are relatively decoupled and damped out by hydrodynamic damping from the significant length of the water column. Thus, the vessel motions and wave hydrodynamic loads imparted on the riser are transferred to the wellhead and subsea hardware. In this paper the fatigue challenges encountered for drilling wells in 530 ft water depth from a sixth generation moored semi-submersible rig are explored. The fatigue loading is critical for the subsea tree connector which is characterized by a high stress amplification factor (SAF). Multiple riser space-out solutions were evaluated including fairings, helically-grooved buoyancy, joints with rope, and modifications to the telescopic joint each of which will be presented in the paper along with combination of different damping parameters to optimize the fatigue performance. The paper will present the subsea tree connector fatigue performance for different riser space-out options and make recommendations for future operations with similar conditions. Other challenges encountered in fatigue evaluation will be discussed. This will highlight the current assumptions and unknowns in data that can form the subject of evaluation for a future joint industry study.

Influence mechanism of running sportswear fatigue based on BP neural network

The fatigue of running sportswear is reflected in the fatigue caused by the tightness of the tights on the skin surface of the limbs, trunk, and other parts during long-term running sports. However, the current research on the fatigue of running sportswear is not deep enough. Therefore, the purpose of this study is to study the mechanism of the fatigue of running sportswear based on BP neural network. This article first takes sportswear as the starting point and uses the surface myoelectricity index as a physiological quantity as a means to combine clothing with sports medicine and sports physiology, breaking the traditional shackles of subjective assessment of fatigue, and giving play to the advantages of interdisciplinary to expand the new direction of the apparel industry and, secondly, use muscle fatigue evaluation method to analyze the muscles of the lower leg under the pressure of sportswear to analyze the strength of the EMG signal and the number of participating sports units and the frequency of discharge synchronization. Experimental data shows that the AUC is 0.756 when wearing sports tights, the sensitivity and specificity are 72% and 19%, and the accuracy is 65%. The experimental results show that clothing pressure affects the fatigue of running sportswear based on BP neural network.