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.
Failure diagnoses on large diesel engine are commonly detected when a deviation or fluctuation in its temperature, pressure, vibration or noise set parameter limits arises. These parameters can be easily monitored and can provide information of the engine’s present state depending on external environment and operating conditions. On the other hand, long term monitoring and condition management can be interfaced into the engine’s existing operating system. The approach is seen to keep track of monitored machines’ status using resonance and vibration amplitude. In particular, these signals will be able to identify complex vibration characteristic pertaining to such as engine torque output and support mounts. In this paper, a basic research for large diesel engine diagnosis was carried-out. The failure diagnosis collects and monitors the vibration state time history by using various vibration signals with reference to ISO 13373-1. Further, this monitoring system in the field of large diesel engines has not been applied practically and the results of this study are presented herein.
In the wire constituting the LIN, which is one of the vehicle communication devices, disconnection of the wire or contact resistance of the circuit occurs due to vibration and aging of the vehicle. This affects the entire communication network and may have a significant impact on the safe driving of the vehicle as information is not transmitted. In this study, a LIN BUS circuit simulator was built on its own like a real car and measured with an automotive oscilloscope instead of a common method of measuring and diagnosing a circuit with a multimeter in the event of a LIN BUS circuit failure. Referring to the experimental results, it will be possible to diagnose faults in circuits efficiently and quickly.
The purpose of this study was to propose useful suggestion by analyzing preventive replacement policy under which there are minor and major failure. Here, major failure is defined as the failure of system which causes the system to stop working, however, the minor failure is defined as the situation in which the system is working but there exists inconvenience for the user to experience the degradation of performance. For this purpose, we formulated an expected cost rate as a function of periodic replacement time and the number of system update cycles. Then, using the probability and differentiation theory, we analyzed the cost rate function to find the optimal points for periodic replacement time and the number of system update cycles. Also, we present a numerical example to show how to apply our model to the real and practical situation in which even under the minor failure, the user of system is not willing to replace or repair the system immediately, instead he/she is willing to defer the repair or replacement until the periodic or preventive replacement time. Optimal preventive replacement timing using two variables, which are periodic replacement time and the number of system update cycles, is provided and the effects of those variables on the cost are analyzed.
The purpose of this study is to present a novel indicator for analyzing machine failure based on its idle time and productivity. Existing machine repair plan was limited to machine experts from its manufacturing industries. This study evaluates the repair status of machines and extracts machines that need improvement. In this study, F-RPN was calculated using the etching process data provided by the 2018 PHM Data Challenge. Each S(S: Severity), O(O: Occurence), D(D: Detection) is divided into the idle time of the machine, the number of fault data, and the failure rate, respectively. The repair status of machine is quantified through the F-RPN calculated by multiplying S, O, and D. This study conducts a case study of machine in a semiconductor etching process. The process capability index has the disadvantage of not being able to divide the values outside the range. The performance of this index declines when the manufacturing process is under control, hereby introducing F-RPN to evaluate machine status that are difficult to distinguish by process capability index.