?????? ????????? ????????? ?????? ???????????? ????????? ????????? ???????????? ??? ????????? ??????

Department of Mehcanical Engineeringclos

????????? ????????? ???????????? ?????? ????????? ?????? ????????? ?????? ????????? ?????? ?????? ????????? ??????

As mechanical systems become more complicated and have diverse sub-modules, various sensor data are collected for the real-time health status monitoring of a system. However, because the collected sensor data are extremely large and contain irrelevant noise to the fault condition of the system, a technique of extracting important data fluctuations should be applied to detect the failure of the system. In general, unsupervised discretization techniques based on data distribution are used to extract fault patterns. However, the methods to extract significant features related to the state changes of a system are not simple. Therefore, we extract fault patterns by applying a supervised discretization method using not only the similarity between measurements but also the system state information. To verify the fault detection performance of the proposed method, acceleration sensor data were collected from a bearing-shaft system and analyzed using the proposed supervised discretized technique

Estimating System State through Similarity Analysis of Signal Patterns

State prediction is not straightforward, particularly for complex systems that cannot provide sufficient amounts of training data. In particular, it is usually difficult to analyze some signal patterns for state prediction if they were observed in both normal and fault-states with a similar frequency or if they were rarely observed in any system state. In order to estimate the system status with imbalanced state data characterized insufficient fault occurrences, this paper proposes a state prediction method that employs discrete state vectors (DSVs) for pattern extraction and then applies a naive Bayes classifier and Brier scores to interpolate untrained pattern information by using the trained ones probabilistically. Each Brier score is transformed into a more intuitive one, termed state prediction power (SPP). The SPP values represent the reliability of the system state prediction. A state prediction power map, which visualizes the DSVs and corresponding SPP values, is provided a more intuitive way of state prediction analysis. A case study using a car engine fault simulator was conducted to generate artificial engine knocking. The proposed method was evaluated using holdout cross-validation, defining specificity and sensitivity as indicators to represent state prediction success rates for no-fault and fault states, respectively. The results show that specificity and sensitivity are very high (equal to 1) for high limit values of SPP, but drop off dramatically for lower limit values