RUL prediction using moving trajectories between SVM hyper planes

With increasing amounts of data being generated by businesses and researchers, there is a need for fast, accurate and robust algorithms for data analysis. Improvements in database's technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis. The primary aim of data mining is knowledge discovery, i.e. patterns in the data that lead to better understanding of the data generating process and to useful predictions. The knowledge that becomes available through data mining enables an asset owner to make important decisions about life cycle costs in advance. In maintenance field, CMMS (Computer maintenance management system) and CM (Condition Monitoring) are the most popular software available in the industries. Since first one stores all historical data, maintenance actions, events and ma nufacturer recommendations, second one collects and stores all critical physical parameters (vibration, temperature.) to be monitored in a regular time basis. However, converting these data into useful information is a challenge. The degradation process of a system may be affected by many unknown factors, such as unidentified fault modes, unmeasured operational conditions, engineering variance, environmental conditions, etc. These unknown factors not only complicate the degradation behaviors of the system, but also make it difficult to collect quality data. Due to lack of knowledge and incomplete measurements, certain important con text information (e.g. fault modes, operational conditions) of the collected data will be missing. Therefore, historical data of the system with a large variety of degradation patterns will be mixed together. With such data, learning a global model for Remaining Useful Life (RUL) prediction becomes extremely hard since the end user does not have enough and good-quality data to model properly the system. This has led us to look for advanced RUL prediction techniques beyond the traditional RUL prediction models. The degradation process for many engineering systems, especially mechanical systems, is irreversible unless the condition is recovered by effective maintenance actions. The irreversible degradation process does not necessarily imply that the observed features will exhibit a monotonic progression pattern during degradation. Such progression pattern is sometimes hard to model using parametric methods. Considering a degradation process involving no or limited maintenance, the process may compose of a sequence of irreversible stages (either discrete or continuous) from new to be worn out, which can be implicitly expressed by the trajectory of the measured condition data or features. Therefore, the RUL of the system can be estimated if its future degradation trend can be projected from those historical instances. In this paper, a novel RUL prediction method inspired by feature maps and SVM classifiers is proposed. The historical instances of a system with life-time condition data are used to create a classification by SVM hyper planes. For a test instance of the same system, whose RUL is going to be estimated, degradation speed is evaluated by computing the minimal distance defined based on the degradation trajectories, i.e. the approach of the system to the hyper plane that segregates good and bad condition data at a different time horizon. Therefore, the final RUL of a specific component can be estimated, and global RUL information can then be obtained by aggregating the multiple RUL estimations using a density estimation method. Proposed model develops an effective RUL prediction method that addresses multiple challenges in complex system prognostics, where many parameters are unknown. Similarities between degradation trajectories can be checked in order to enrich existing methodologies in prognostic's applications. Existing CM data for bearings will be used to verify the model.