Yeosu National Industrial Complex is one of Korea’s representative petrochemical industrial complexes where crude oil refining and petrochemical companies are concentrated. According to the results of the 2021 chemical emissions survey, during the process of manufacturing, storage, and transportation at the Yeosu National Industrial Complex, various hazardous chemicals, including hazardous air pollutants, volatile organic compounds and odorous substances are being emitted into the air, affecting the surrounding environment and the health of residents. The Ministry of Environment is applying strengthened standards by designating the Yeosu National Industrial Complex as an air conservation special measure area and establishing odor management areas to manage the air environment. Nevertheless, odor complaints continue to be registered and related complaints increase when turnaround work is carried out. Since air emissions are not counted during periods of turnaround as normal operations are temporarily suspended, it was difficult to establish policies to reduce odor complaints because the source of emissions and emission quantities cannot be ascertained with certainty. In this study, the extensive Yeosu National Industrial Complex was subdivided into 4 areas using a mobile vehicle equipped with PTR-ToF-MS capable of real-time analysis without sample pretreatment being carried out. Measurements were repeated during the day, night, and dawn while moving around the internal boundary of the plant and the boundary of each region where turnaround activities were being carried out. As a result, the recorded measurement for acrylonitrile was the highest at 6340.0 ppb and propyne and propene were measured the most frequently at 128 times each. Based on these results, it will be possible to help reduce emissions through process improvement by efficiently operating air measurement networks and odor surveys that conduct regular measurements throughout the year and providing actual measurement data to the plant. Also, it will help reduce odor complaints and establish systematic air management policies.

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

This paper proposes a standardized vehicle body repair manual for vehicles with major damage to the rear side members caused by rear-end collisions. The manual is used to refine the vehicles. Typical work involves replacing large traffic accident modes with new members or modifying and aligning existing members. If repairs are made for various reasons, the members should be attached based on the body of a new vehicle. Unlike new vehicle members, the accident vehicle repair process depends heavily on the operator's skilled skills (i.e., the performance, purpose, level, and quality of the body replacement work due to the nature of the vehicle body replacement work). When repairing or replacing a rear side member of a body repair, three methods of operation are applied because the damage and deformation vary depending on the object of the other party, although the degree of damage varies. There is no standardized manual for side member over-hole replacement, partial replacement, or partial modification, since these repair methods vary depending on the operator's thoughts and angle of view. Therefore, customers should use standardized vehicle repair manuals to ensure that their vehicle receives the same repair when damaged. This study is expected to develop a standardized vehicle repair manual to reduce the drop in used market prices after replacement or repair of rear side members at vehicle repair plants.

낚시어선 제도를 도입하게 된 근본 배경에는 평상시 어선으로 순수 어업활동을 영위하다 특정한 시기(금어기 등)에 한해 낚시 어선업을 겸업할 수 있도록 하여 영세 어업인의 경제활동에 대한 보조 수단적 자격을 부여하기 위한 것으로 하고 있다. 그리고 여기에서 의 낚시어선은 「어선법」에 따라 등록된 어선을 사용하여 유선(遊船) 행위를 할 수 있도록 한 것이므로 낚시어선의 형태 또한 관련 규정 에 따라 실질적으로 현장에서 어로활동을 하기에 용이한 일반 보편적인 구조를 가지고 있어야 한다. 그러나 현재 대부분의 낚시어선업자 는 소득증대에만 중점을 두면서 일반적인 어선 본래의 용도에 맞게 합당한 형태로 낚시어선을 건조하기보다는 낚시어선업에 치우친 편 향된 선체구조를 가지는 등 편법에 준하는 비정상적인 선형을 선호하고 있다. 그 결과, 전체 어업활동 중 낚시어선업을 일부 겸업(부업) 정도로만 여기고 있는 어선 세력들과의 갈등[정부 지원책(면세유 공급 등)에 대한 상대적 형평성 훼손 및 생계형 어족자원 고갈 등]은 물 론이고 안전관리에 있어서도 심각한 문제를 일으키고 있는 실정이다. 한편, 이 같은 문제를 야기 시키는 가장 근본적인 원인은 현행 「낚 시 관리 및 육성법」에서 낚시어선의 개념을 「어선법」에 따라 등록된 어선으로 제한하고, 또한 이에 따른 검사기준 등을 적용하는 것 에서 비롯되고 있다 할 것이다. 이에 따라 본 논문에서는 낚시어선의 분포 현황, 구조적 특성, 낚시어선의 운용실태 및 정부의 낚시진흥 정책 등에 대한 분석을 통해 낚시어선에 대한 개념을 현실정에 맞게 관련 법제(규정) 등을 재정비하여 현재의 낚시어선을 어선으로부터 완전히 분리시켜 낚시전용 선박으로 운용하기 위한 개선방안을 제시하고자 한다.

Cars serve as vehicles for the conveyance of both passengers and cargo. Inevitably, traffic accidents constitute a significant facet of vehicular operation. These accidents manifest in various forms, including frontal, rear-end, and lateral collisions. While the resultant vehicular damages may exhibit similarities, they remain inherently distinct. Owing to the intricate nature of automotive body repairs, simplistic adherence to textbook doctrines proves inadequate. The rectification of damaged vehicle bodies hinges upon the practitioner's experiential acumen. Consequently, discourse pertaining to body repair technology necessitates grounding in empirical data encompassing prevailing industry norms and attendant financial implications. Variability in individualized methodologies can engender substantial temporal and monetary outlays within the domain of automotive bodywork. Moreover, the integration of novel material technologies within vehicular structures mandates a perpetual pursuit of knowledge and empirical inquiry into the domain of vehicle body repair procedures, particularly as applied to emerging materials. Compounding this imperative is the unwavering commitment to preserving the safety paradigm from the vehicle owner's perspective, ensuring that restorative interventions subsequent to accidents do not compromise safety benchmarks.

Car accidents require continuous access to new technologies in the field of maintenance that cannot be achieved by textbook theory alone due to the nature of body repair without the same damage and repair conditions. In the case of vehicle repairs due to unexpected accidents, it is difficult to satisfy the needs of the vehicle owner, so in this study, it is possible to restore them to their original state with improved technology like the vehicle owner. Better maintenance technologies have been explored: complete replacement of side quarter panels, partial replacement and partial modification of side quarter panels, as well as tangible and intangible effects such as not applying depreciation rates due to traffic accidents, preventing environmental pollution and maximizing owner satisfaction.

This study is to identify the maintenance service quality of eco-friendly cars, which are rapidly increasing recently. Research is conducted by synthesizing research from the perspectives of internal employees and external customers by using the service profit chain model. Specifically, it is to study the overall structural relationship between internal customer satisfaction, physical quality, interaction quality, outcome quality, external customer satisfaction and long-term orientation. For the study, 202 questionnaires were collected from internal employees and 204 questionnaires from external customers. The results of testing the research hypotheses targeting the internal employee group are as follows. As a result of testing hypothesis 1, internal customer satisfaction has a significant positive (+) effect on physical quality and interaction quality. As a result of testing hypothesis 2, the service quality of eco-friendly car maintenance has a significant positive (+) effect on each other. As a result of testing hypothesis 3, physical quality and outcome quality have a significant positive (+) effect on external customer satisfaction. The results of testing the research hypotheses targeting an external customer group are as follows. As a result of testing hypothesis 2, in the relationship between eco-friendly car maintenance service quality, physical quality has a significant positive (+) effect on interaction quality, and interaction quality has a significant positive (+) effect on outcome quality. As a result of testing hypothesis 3, interaction quality and outcome quality have a significant positive (+) effect on external customer satisfaction. As a result of testing Hypothesis 4, external customer satisfaction has a significant positive (+) effect only on intention to reuse. Finally, as a result of examining the difference in perception between the internal employee group and the external customer group in eco-friendly car maintenance service quality and external customer satisfaction, it was verified that there was a significant difference only in outcome quality and external customer satisfaction.

This study identified the core competencies of aircraft maintenance quality engineers and compared the importance and retention of core competencies. Through literature research, 21 core competencies were derived in three areas of management technology, elemenal technology and collaboration technology, and a survey was conducted on the importance and retention of core competencies for 42 aircraft maintenance quality engineers. As a result of the survey, the importance of all core competencies of aircraft maintenance quality engineers is 3.95/5 on average, and the retention of all core competencies is 3.99/5 on average. 'Risk Management’, ‘Creating Document’, ‘Honesty/Moral’ were identified as the most important competencies in each area, and ‘Quality Management’, ‘Language’, ‘Honesty/Moral’ were identified as the most possessed competencies in each area. An IPA (Importance-Performance Analysis) was performed to analyze the details. Through IPA, ‘Risk Management’ and ‘Safety Management’ were evaluated as having a low degree of retention compared to a high level of importance. Therefore, they were identified as a core competencies that need to be improved first. In addition, the characteristics of each core competency and the recognition level in the field were also identified. This study will be helpful in defining the roles and functions of aircraft maintenance quality engineers to improve flight quality and prevent aviation accidents.

Traffic accidents that occur while driving a car are inevitable, and the characteristics of the body repair field are that each vehicle accident has the same damage and no same repair conditions. Therefore, it is difficult to acquire know-how in vehicle body repair simply by theoretical method of textbook, and the repair method must be changed as new materials and new technologies are applied to it. In particular, roof panels are not frequently damaged by traffic accidents, but they are damaged by falling objects from the roof of a building or the opposite lane, resulting in roof bending and vehicle repair using dent repair to minimize VOC and reduce costs. In this study, the optimal maintenance method is proposed by analyzing the time, cost, and waste generation according to the work method when the degree of damage is large and replacement is required

Considering the features of body repair for automotive body where same damage and repair conditions does not exist in car accident, it is essential to acquire know-how and continuously approach new materials and new technologies on the site of maintenance where the theoretical instruction of textbook cannot react accurately minute by minute. Especially, in case of car repair from unexpected accidents such as body repair or dent repair, it is difficult to satisfy vehicle owners despite their request. Accordingly, this study researched the better maintenance technology to restore to its original state with more improved technology as vehicle owners wish. This study made time to repair reduced by 98% and the cost reduced by 79.6% through the accurate diagnostic technique before repair and maintenance technologies of body crash analysis applying property of high-speed tensile. It also obtains intangible effects including prevention of environmental pollution and maximization of vehicle owners satisfaction.

This paper addresses the maintenance optimization problem in multi-component systems in which parts are connected in series, carrying out several missions interspersed with scheduled finite breaks. Due to limited time or resources, maintenance actions can be only carried out on a limited set of components. The decision maker then has to decide which components to maintain to ensure a pre-specified performance level during next mission. Most of the existing models in the literature usually assume only one system and enough spare parts. However, there are situations in which maintenance is required for multiple systems of the same type. To overcome this restrictive assumption, this study optimizes the maintenance problem considering the lack of repair parts and cannibalism for many identical systems. This study presents two optimization models with different objectives to solve the problem and analyzes the results so that the decision maker can decide. The results of this study are expected to be used for the maintenance of multiple systems of the same type, such as swarm drones.

This research is to study the solution to the defects in maintenance and inspection that can be predicted/prevented in advance among human factors that account for more than 70% of the causes of aviation accidents. Traditionally, mechanics have performed visual inspections of aircraft exteriors. Due to this, there were factors that affect the human ability of mechanics in aircraft maintenance and inspection, safety problems when performing the upper part of the aircraft inspection, and the difficulty of precise inspection. To improve these problems, we conduct a study on an AI drone inspection system that has deep-learned samples on aircraft damage/defects. In this paper, we describe the aircraft maintenance inspection checklist, non-destructive inspection types, types of aircraft damage and defects, deep-learning highly reliable AI drone inspection systems, and the expected effects of this technology and future applications. Through this system research, it is expected that mechanics will efficiently inspect the aircraft through the optimization of aircraft maintenance system technology to prevent aviation accidents in advance and reduce time and economic costs.

Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.