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.

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.

OBJECTIVES : The objective of this research is to determine the integrity of pavement structures for areas where voids exist. Furthermore, we conducted the study of voided-area analysis and remaining life prediction for pavement structures using finite element method. METHODS : To determine the remaining life of the existing voided areas under asphalt concrete pavements, field and falling weight deflectometer (FWD) tests were conducted. Comparison methods were used to have better accuracy in the finite element method (FEM) analysis compared to the measured surface displacements due to the loaded trucks. In addition, the modeled FEM used in this study was compared with well-known software programs. RESULTS : The results show that a good agreement on the analyzed and measured displacements can be obtained through comparisons of the surface displacement due to loaded trucks. Furthermore, the modeled FEM program was compared with the available pavement-structure software programs, resulting in the same values of tensile strains in terms of the thickness of asphalt concrete layers. CONCLUSIONS: The study, which is related to voided-area analysis and remaining life prediction using FEM for pavement structures, was successfully conducted based on the comparison between our methods and the sinkhole grade used in Japan.

국내 고속도로의 교량은 2000년 이후 집중된 선형개량 및 신규 노선 증가 사업으로 10년 전과 비교하여 2배 이상 증가하였다. 이에 따라 유지관리 비용도 지속적으로 증가하고 있다. 현재 고속도로 유지관리 예산 비중이 가장 높은 항목은 아스팔트 교면 교량의 콘크리트 바닥판 열화에 의한 보강 부분이다. 2011년 고속도로 관리교량은 약 7,800여개에 도달한 시점에서 현재 방법으로는 향후 어느 정도 바닥판 보강 예산이 필요한지 어느 시기에 증액을 하여야되는지 명확하게 추정하기 어렵다. 본 연구에서는 신뢰도 분석 방법인 와이불 분포에 의한 생존 수명 예측 기법을 적용하여 현재 고속도로 아스팔트 계열의 교면 교량의 평균 수명을 추정하였고 이를 토대로 향후 예상 보강 비용을 추정하였다.

공항 콘크리트 포장은 설계기준을 역순으로 하거나, FWD 장비로부터 얻어진 탄성 계수를 근거로 포장체의 잔존수명을 추정하여 왔었다. 그러나 FWD로부터 얻어진 탄성계수 값은 역산 방법에 따라 변동성이 심하므로 잔존수명의 일관성이 결여된 한계가 있다. 또한 구조적 측면만 고려하여 포장의 상태를 평가하므로. 기능적 측면을 반영하지 못하는 한계가 있었다. 따라서 본 연구에서는 공항 콘크리트 포장의 잔존수명 산출에 있어서 구조적 측면 및 기능적 측면 모두를 고려하는 논리를 제시하였으며, 각 논리별 세부 절차별에 있어서의 기준 및 모델 적용을 제안하였다 구조적 잔존수명 추정 논리를 개선하기 위해 오래된 공항을 대상으로 하여 하중을 받은 구간과 받지 않은 구간에서의 잔존수명 추정 인자 선정을 위한 실험을 실시하였다. 그 결과 기존에 주로 사용되어 왔던 탄성계수보다는 하중전달효율이 잔존수명 추정 인자로 규명되었다. 새롭게 개발한 잔존수명 추정 논리 및 세부 모형들의 현장 적용성을 파악하기 위하며 오래된 공항 한 개소를 선정하였다. 새로운 논리에 따라 현장 실험 및 분석을 수행한 결과 실무에 적용하는데 무리가 없음을 알 수 있었다.

한국시설안전공단에서는 ‘시설물의 안전관리에 관한 특별법’에 따라 철근콘크리트 구조물의 안전점검 및 정밀안전진단을 실시하도록 제시하고 있다. 그러나 한국시설안전공단 안전점검 및 정밀안전진단 세부지침의 평가방법에서는 평가결과를 등급으로 제시하기 때문에 구조물의 잔존수명을 알 수 없으며 부등침하가 구조물의 잔존수명에 미치는 영향을 반영하지 못한다. 따라서, 이 연구에서는 부등침하의 영향이 반영된 구조물의 잔존수명 평가모델을 제시하고자 하였다. 부등침하와 각 변위의 상관관계를 나타내는 기존의 연구를 바탕으로 부재의 공칭강도에 부등침하의 영향을 반영시키기 위한 식을 제시하였으며, 실제 철근콘크리트 구조물의 현장데이터를 활용하여 부등침하가 구조물의 잔존수명에 미치는 영향을 분석하였다.

The remaining service life (RSL) of the concrete structures built in the past has become a social issue with the concerns of the sustainable construction. In the previous studies, some simple methods for estimation of the RSL of the concrete structures were proposed. However, most of the existing studies on the RSL evaluation method have focussed on the investigation of the single deteriorating factor. In this study, the combination effect of various factors related with durability performances of the concrete structure, such as concrete carbonation and chloride penetration were considered by utilizing the fuzzy and reliability theory.