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.

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

다양한 산업에서 강조되고 있는 정비의 중요성은 각 분야에 다양한 정비전략을 적용하도록 만들었다. 해양산업 역시 그에 따른 정비전략의 변화가 있었으나 타 산업 대비 그 속도가 느려 실제 적용이 되지 않은 채 과거 시행되고 있던 방식을 유지하는 경우가 많다. 특히 선박은 기존에 행해왔던 방식의 정비전략을 사용하고 있는 편이며 해상의 조건에서 선박은 새로운 정비전략의 개발을 필요로 하고있다. 이에 선박예지정비모델은 기기의 정비가 필요한 시점을 예지하여 조치할 수 있는 정비전략으로서 선박이 항해 중에 처할 수 있는 정비 관련 위험요소들을 줄여 주는 모델이다. 본 연구는 선박예지정비모델의 개발을 위한 연구 중의 하나로서, LNG선박 입거사양서의 텍스트 데이터 분석을 통한 결과를 원문의 내용을 바탕으로 해석해보았다. 공통된 정비항목 조합을 도출하여 선박 내 다른 기기들 사이에 작용하고 있는 상호연관성을 발견하고 이를 앞으로 개발될 선박예지정비모델에 적용하고자 한다.

해양 운송 산업은 특성상 항공 및 철도 등의 다른 운송 산업보다 비교적 늦게 신기술이 적용되는 산업이다. 현재 대부분의 선박은 기계장치 및 시스템에 문제가 발생하거나 운용 시간 기반으로 정비를 하는 사후 정비(Corrective Maintenance, CM)와 예방 정비 (Preventive Maintenance, PM)에 속하는 시간 기반 정비(TBM, Time Based Maintenance)가 적용되고 있다. 그러나 높은 유지보수 비용이 요구되고, 육상의 즉각적인 지원이 어려우며, 선박이 멈추면 즉시 위험에 노출되는 해양 환경에서 운영되는 선박에서 과도한 단순 정비로 인한 인력과 비용 낭비, 예측되지 못한 고장 및 결함으로 유발되는 사고 등으로 인해 운용 효율화 측면에서 기존 정비법에 대한 한계점이 문제시 되고 있다. 예지 정비(Predictive Maintenance, PdM)는 진보된 기술로 기계의 상태 및 성능을 모니터링하여 고장시기를 예측하여 정비하는 방법으로 핵심 기계장치가 항상 최상의 작동 상태를 효율적으로 유지할 수 있도록 한다. 본 논문은 해양 환경에서 PdM의 적용성에 중점을 둔 해양 예지 정비(MPdM, Maritime Predictive Maintenance)에 대해 고안하였으며, 제시된 MPdM은 지리적 고립과 극한 해양 상황 등 해양 운송 산업의 특수한 환경을 고려하여 설계되었다. 본 논문은 선진 미래 해양 운송을 가능하게 하는 MPdM이라는 개념과 그 필요성을 제안한다.

전 세계 90 %의 인구가 WHO의 연평균 미세먼지 노출 기준(10 ㎍/㎥)을 초과한 공기를 흡입하고 있다. 전 세계적으로 육상뿐 만 아니라 해양에서 발생하는 질소산화물에 대한 규제를 통해 2차 오염물질, 초미세먼지 저감에 대해 노력하고 있으며 국내에서는 선박에서 미세먼지 발생의 주요한 원인인 황 함유량 저감과 환경친화적 선박의 개발 및 보급 등을 통해 깨끗한 해양환경 조성을 위한 노력을 하고 있다. 디젤엔진 유해 배출가스 저감을 위한 기술 중 압력 손실이 적고 높은 집진 효율 및 NOx의 제거와 유지 관리의 장점이 많은 전기 집진기의 수요와 중요성이 증가하고 있다. 본 연구는 총톤수 999톤급 선박의 2,427 kW 선박용 디젤엔진의 미세먼지 저감을 목적으로 개발된 전기 집진기를 예지보전단계에서 고장모드영향분석을 통해 장비 품질을 높여 선박 내에서의 내구연한을 높이고자 위험 우선순위 도출하였다. 위험 우선순위는 고장모드 241(poor dust capture efficiency)이 RPN 180으로 가장 높았다. Collecting electrode 에서 가장 많은 고위험 고장모드를 검출하여 집중관리 부품으로 관리해야 할 필요가 있었으며 원인으로 진동과 핀 풀림으로 인한 유 격 불량이 가장 많이 검출되었다. 핀 풀림 역시 근본적으로는 선체 또는 장비에서 발생하는 진동이 원인이 되어 발생할 수 있는 사항이기 때문에 핀 풀림이 발생하는 개소에 보완이 필요하겠다.

Purpose: This study was tried to identify the effects of simulation program by applying hazard perception training on self-efficacy of patient safety, error recovery and problemsolving process in nursing students. Methods: A nonequivalent control group designed was used. The study was composed of hazard perception training and simulation program. Sixteen teams of a total of 61 nursing students participated in the simulation program using a high fidelity simulator. The collected data were analyzed by descriptive statistics, χ2-test and t-test using PASW 18.0 program. Result: There were statistically signigicant in self-efficacy of patient safety(t=2.55, p=.013), error recovery(t=2.82, p=.007), and problem-solving process(t=3.29, p=.002) in the experimental group. Conclusion: These results indicate that the simulation program by applying hazard perception training is effective in improving self-efficacy of patient safety, error recovery and problem-solving process for nursing students. Further study is recommended to confirm the long-term effects of the simulation program by applying hazard perception training.

Since the basic built-in-test, prognostic health management (PHM) has evolved into more sophisticated and complex systems with advanced warning and failure detection devices. Aerospace and military systems, manufacturing equipment, structural monitor- ing, automotive electronic systems and telecommunication systems are examples of fields in which PHM has been fully utilized. Nowadays, the automotive electronic system has become more sophisticated and increasingly dependent on accurate sensors and reliable microprocessors to perform vehicle control functions which help to detect faults and to predict the remaining useful life of automotive parts. As the complication of automotive system increases, the need for intelligent PHM becomes more significant. Given enormous potential to be developed lays ahead, this paper presents findings and discussions on the trends of automotive PHM research with the expectation to offer opportunity for further improving the current technologies and methods to be applied into more advanced applications.