우리나라의 승강기 보유 대수는 2023년 12월 기준 약 84만 대로 세계 7위에 달하며, 매년 약 3.1만 대의 신규 승강기가 설치되어 세계 3위의 승강기 대국으로 자리 잡고 있다. 승강 기의 지속적인 증가와 함께 고장 및 사고도 꾸준히 증가하고 있으며, 2022년 소방청의 구조 활동 분석에 따르면 승강기 사고 및 고장으로 인한 구조 인원은 23,856명에 달하고, 119 출동 건수는 33,114건에 이른다. 이로 인해 발생한 사회적 안전 비용은 약 340억 원으 로 추정된다. 본 연구는 2019년부터 2023년까지 최근 5년간 한국승강기안전공단에 접수 된 96,577건의 고장 데이터와 330건의 사고 데이터를 활용하여 승강기 고장과 사고 간의 상관관계를 분석하였다. 지역별, 용도별, 연도별 분석을 통해 고장률과 사고비율 간의 관 계를 파악하고, 통계적 상관분석을 통해 고장이 반드시 사고로 이어지지 않음을 확인하였 다. 특히 고장률과 사고율 사이에 명확한 양의 상관관계가 나타나지 않았으며, 일부에서는 음의 상관관계가 도출되었다. 이러한 결과는 승강기 고장과 사고가 개별적으로 관리되어 야 하며, 고장이 발생하더라도 철저한 유지관리와 안전조치에 따라 사고 발생은 예방 가능 하다는 점을 시사한다.

Fault detection in electromechanical systems plays a significant role in product quality and manufacturing efficiency during the transition to smart manufacturing. Because collecting a sufficient number of datasets under faulty conditions of the system is challenging in practical industrial sites, unsupervised fault detection methods are mainly used. Although fault datasets accumulate during machine operation, it is not straightforward to utilize the information it contains for fault detection after the deep learning model has been trained in an unsupervised manner. However, the information in fault datasets is expected to significantly contribute to fault detection. In this regard, this study aims to validate the effectiveness of the transition from unsupervised to supervised learning as fault datasets gradually accumulate through continuous machine operation. We also focus on experimentally analyzing how changes in the learning paradigm of the deep learning model and the output representation affect fault detection performance. The results demonstrate that, with a small number of fault datasets, a supervised model with continuous outputs as a regression problem showed better fault detection performance than the original model with one-hot encoded outputs (as a classification problem).

선박의 추진전동기는 소량 주문생산되어 고장진단을 위한 신호를 사전에 확보하는 것이 불가능하다. 운용기간 중 계측을 통해 데이터를 확보하는 것은 많은 시간과 비용을 초래하기에 물리모델을 통해 데이터를 확보하는 것이 유일한 방법이다. 물리모델을 통해 얻 은 데이터를 고장진단에 활용하기 위하여 데이터의 정확도를 확보해야 한다. 기존 전동기 물리모델의 경우 전동기에서 발생하는 구조-전 기 연성효과를 온전히 고려하지 않아 진동데이터의 해석 오차가 발생하는 것을 확인할 수 있다. 본 논문에서는 구조-전기 완전연성 물리 모델을 개발하여 물리모델데이터의 정확도를 개선하였다. 실험계측 데이터와 물리모델 데이터의 비교를 통해 전동기 상태별 데이터를 높 은 정확도로 획득할 수 있음을 확인하였다. 본 논문에서 제시한 구조-전기 완전연성 물리모델을 이용하여 정상상태와 결함상태에서 나타 나는 진동수준을 예측할 수 있음을 확인하였으며, 구조-전기 완전연성 반영 필요성을 입증하였다.

Automotive technology has developed rapidly and is becoming the intensive of cutting edge technology. For this reason, Automotive are used not only as a means of transportation, but also as a private and leisure spaces. The driver wants to keep quiet even if the car is used for a long time. NVH should be reduced because it is caused by mechanical defects and aging. In this study, it was presented that a seven-step procedure for failure diagnosis and repair to reduce noise/vibration. NVH was diagnosed by comparing the result of the rotator order tracking analysis with the problem frequency. It was possible to accurately analyze the cause of noise and vibration, also it coud identify the location, and repair that.

Bearing-shaft systems are essential components in various automated manufacturing processes, primarily designed for the efficient rotation of a main shaft by a motor. Accurate fault detection is critical for operating manufacturing processes, yet challenges remain in sensor selection and optimization regarding types, locations, and positioning. Sound signals present a viable solution for fault detection, as microphones can capture mechanical sounds from remote locations and have been traditionally employed for monitoring machine health. However, recordings in real industrial environments always contain non-negligible ambient noise, which hampers effective fault detection. Utilizing a high-performance microphone for noise cancellation can be cost-prohibitive and impractical in actual manufacturing sites, therefore to address these challenges, we proposed a convolution neural network-based methodology for fault detection that analyzes the mechanical sounds generated from the bearing-shaft system in the form of Log-mel spectrograms. To mitigate the impact of environmental noise in recordings made with commercial microphones, we also developed a denoising autoencoder that operates without requiring any expert knowledge of the system. The proposed DAE-CNN model demonstrates high performance in fault detection regardless of whether environmental noise is included(98.1%) or not(100%). It indicates that the proposed methodology effectively preserves significant signal features while overcoming the negative influence of ambient noise present in the collected datasets in both fault detection and fault type classification.

This study is to deal with a failure phenomenon that occurred during a vibration test on an Inertial Navigation System mounted on a self-propelled howitzer. Vibration occurs naturally due to the operation characteristics of self-propelled howitzers, The study describes a case of failure that occurred during the durability verification process. It explains the function and configuration of the INS(Inertial Navigation System) and describe how the failure occurred through understanding the phenomenon. Based on the occurrence phenomenon, an in-depth cause analysis was conducted and fundamental improvement measures were presented to prevent recurrence. It is expected that this study will aid as a reference for problem solving when similar failures occur in the future.

This study introduces a novel approach for identifying potential failure risks in missile manufacturing by leveraging Quality Inspection Management (QIM) data to address the challenges presented by a dataset comprising 666 variables and data imbalances. The utilization of the SMOTE for data augmentation and Lasso Regression for dimensionality reduction, followed by the application of a Random Forest model, results in a 99.40% accuracy rate in classifying missiles with a high likelihood of failure. Such measures enable the preemptive identification of missiles at a heightened risk of failure, thereby mitigating the risk of field failures and enhancing missile life. The integration of Lasso Regression and Random Forest is employed to pinpoint critical variables and test items that significantly impact failure, with a particular emphasis on variables related to performance and connection resistance. Moreover, the research highlights the potential for broadening the scope of data-driven decision-making within quality control systems, including the refinement of maintenance strategies and the adjustment of control limits for essential test items.

In recent automated manufacturing systems, compressed air-based pneumatic cylinders have been widely used for basic perpetration including picking up and moving a target object. They are relatively categorized as small machines, but many linear or rotary cylinders play an important role in discrete manufacturing systems. Therefore, sudden operation stop or interruption due to a fault occurrence in pneumatic cylinders leads to a decrease in repair costs and production and even threatens the safety of workers. In this regard, this study proposed a fault detection technique by developing a time-variant deep learning model from multivariate sensor data analysis for estimating a current health state as four levels. In addition, it aims to establish a real-time fault detection system that allows workers to immediately identify and manage the cylinder’s status in either an actual shop floor or a remote management situation. To validate and verify the performance of the proposed system, we collected multivariate sensor signals from a rotary cylinder and it was successful in detecting the health state of the pneumatic cylinder with four severity levels. Furthermore, the optimal sensor location and signal type were analyzed through statistical inferences.

In this paper, as there are many cases of fires occurring due to the failure or inoperability of the thermostat of electronic products, the purpose is to test and analyze the risks and probabilities through fire cases and reproduction experiments, and suggest countermeasures. Among electronic products, water purifiers are composed of a refrigerant system with a compressor to make cold water, a heating device to make hot water, and an electric device used as an energy source. Due to the nature of the water purifier manufacturing, these devices are subject to a lot of moisture and dust. etc. exist in large quantities and use electrical energy, so there is a possibility of fire due to short circuit in the wire, electrical abnormal overheating (tracking phenomenon) in the thermostat, electronic board, starting relay, etc., and overheating of the heating device (Band Heater). there is. Therefore, in order to prevent fires from these devices, a system to remove foreign substances inside the water purifier is necessary, the use of heat-resistant (fire-resistant) wires for electrical devices is essential, and the use of non-combustible materials (semi-combustible materials) for each part is necessary to prevent fire. The risk must be eliminated through prevention and combustion expansion prevention devices.

PURPOSES : As the number of fixed traffic enforcement equipments increase rapidly, it is necessary to improve efficient operation and management plans. The aim of this study is to evaluate the factors influencing fixed traffic enforcement equipment failure. METHODS : This study utilized binary logistic regression analysis using the database provided by the Korean Road Traffic Authority to evaluate the factors affecting the failure of fixed traffic enforcement equipment. RESULTS : As a result of the evaluation of this study, the main factors affecting failure were red-light camera, old equipment, Jeju, National expressways, and equipment with low speed limits. CONCLUSIONS : This study can be used as basic data on the improvement of operation management plas for maintenance of traffic enforcement equipment. Through this study, it will be possible to establish a step-by-step plan with high efficiency comapared to the input of required manpower.

원자력발전소 지진 확률론적 안전성 평가인 PSA(Probabilistic Safety Assessment)는 오랜 기간에 걸쳐 확고히 구축되어 왔다. 반면 에 다양한 공정 기반의 산업시설물의 경우 화재, 폭발, 확산(유출) 재난에 대해 주로 연구되어 왔으며, 지진에 대해서는 상대적으로 연 구가 미미하였다. 하지만, 플랜트 설계 당시와 달리 해당 부지가 지진 영향권에 들어갈 경우 지진 PSA 수행은 필수적이다. 지진 PSA 를 수행하기 위해서는 확률론적 지진 재해도 해석(Probabilistic Seismic Hazard Analysis), 사건수목 해석(Event Tree Analysis), 고장수 목 해석(Fault Tree Analysis), 취약도 곡선 등을 필요로 한다. 원자력 발전소의 경우 노심 손상 방지라는 최우선 목표에 따라 많은 사고 시나리오 분석을 통해 사건수목이 구축되었지만, 산업시설물의 경우 공정의 다양성과 최우선 손상 방지 핵심설비의 부재로 인해 일 반적인 사건수목 구축이 어렵다. 따라서, 본 연구에서는 산업시설물 지진 PSA를 수행하기 위해 고장수목을 바탕으로 확률론적 시각 도구인 베이지안 네트워크(Bayesian Network, BN)로 변환하여 리스크를 평가하는 방법을 제안한다. 제안된 방법을 이용하여 임의로 생성된 가스플랜트 Plot Plan에 대해 최종 BN을 구축하고, 다양한 사건 경우에 대한 효용성있는 의사결정과정을 보임으로써 그 우수 성을 확인하였다.

We have observed a phenomenon where the internal X capacitors of the input EMI filter experienced damage during operation. To solve the problem, we have analyzed the malfunction by identifying the characteristics and operating principles of EMI filter. Based on this analysis, we have derived improvement strategies and validated them through experiments. This paper help some people prevent the similar problem when developing the similar equipment and solve the similar problem of the similar equipment.

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.

The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.