Machine health monitoring for cyber-physical systems

Estimating the failure time of the machinery that are used in the production is crucial to achieve an efficient maintenance in Industry $4.0$ era. Remaining useful life (RUL) is the term that refers to the length of time in terms of the raw time intervals or usage that a machine will continue to operate before it requires a repair or replacement. Machine learning (ML), especially deep learning, provides industry practitioners with efficient tools for estimating the RUL. However, ML is far from being fully utilized, since domain knowledge is generally ignored in current studies. This thesis focuses on three main domain specific problems in machine condition monitoring to improve the performance of ML based RUL estimation. First, RUL is ill-defined during the healthy operation period of the machinery, hence enforcing ML with respect to a fictitious true RUL during these periods adversely affects the overall RUL estimation accuracy. In this thesis, a system level anomaly detection triggered RUL estimation method is proposed to detect degradation onset point in sensor data to prevent ML models to estimate RUL in this period, and hence to increase the accuracy. Secondly, the operating conditions of the machines affect their degradation pattern and related sensor measurements. Thus, the accuracy of ML based RUL estimation models decreases when the machinery operate in varying conditions. A siamese neural network based operating condition-invariant feature extraction method is introduced to alleviate this problem. These two approaches are verified using a benchmark turbofan engine degradation data. Lastly, most of the ML models suffer from lack of data in RUL estimation. If the data are high dimensional such as image, profile, etc., the problem becomes more challenging. Two deep learning architectures are proposed to resolve curse of dimensionality in case of degradation data scarcity. Efficiency of the proposed models is demonstrated with an infrared image data.