ProgMachina: Feature Extraction and Processing Package for Prognostic Studies

Abstract

Prognostic studies of industrial systems essentially focus on health deterioration analysis that has recently been oriented toward data analytics and learning systems. In general, real degradation phenomena suffer from complex drifted data in which degradation patterns are hidden and change over time. Accordingly, such a process requires a well-structured processing and extraction mechanism to reveal such patterns, which facilitates the transition to other model reconstruction and investigation tasks. In this context, to provide additional simplicity of data processing in the field, a complete software package is designed and grouped into a single function that is fully automated and does not require human intervention. The package named ProgMachina (i.e., prognostic machine) provides a featured list of processed features from a life cycle that passed through denoising, filtering, outlier removal, and scaling process to ensure data significance in terms of degradation. The package allows for the use of a time window with a specific overlap to ensure that the scanning process of all possible degradation patterns is properly done. Additionally, an exponential function is used to identify a corresponding health index of degraded signals. In addition, a set of well-known metrics is used to assess the degradation of extracted features. Data visualization and many previous experiments on machines show the effectiveness of such a methodology in terms of obtained prediction accuracy and degradation assessment. The package is designed with Matlab software and made available online to be exploited in similar fields.

Keywords

• degradation
• feature extraction
• health index
• machine learning
• prognostics and health management
• remaining useful life