IMAGE FUSION QUALITY ASSESSMENT USING SHANNON ENTROPY AND HARTLEY USEFUL INFORMATION COEFFICIENT

This paper demonstrates how to construct a complete Remaining Useful Life (RUL) estimation workflow,including the steps of preprocessing, selecting trend features, constructing a health indicator by fusing sensors,training RUL similarity estimators, and verifying the prediction performance. The method was tested in aMATLAB demo program implementing this method for predicting the occurrence of faults in technical systems(https://www.mathworks.com/help/predmaint/ug/similarity-based-remaining-useful-life-estimation.html) basedon data from the "PHM08 Challenge Data Set", NASA Ames Prognostics Data Repository(http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA. Themethod is focused on the use of reasonable technical characteristics of the equipment being estimated, which aresufficiently covered in the reference literature. Therefore, the method gives good results when assessing equipmentwhose operating conditions are close to the statistical average. This paper uses the Predictive MaintenanceToolbox™ in MATLAB, which includes several specialized models developed for calculating RUL from varioustypes of measured system data. These models are useful when you have historical data and information, such as:‒ failure histories of machines similar to the one to be diagnosed. The historical data for each member of thedata ensemble is fitted to a model of identical structure; ‒ a known threshold value of some condition indicatorindicating failure; ‒ data on how much time or how much use it took for similar machines to fail (service life).RUL estimation models provide methods for training a model using historical data and using it to make a remainingservice life prediction. The term service life here refers to the useful life of a machine defined in terms ofany quantity used to measure the service life of a system. Similarly, time evolution can mean the evolution of avalue with usage, distance traveled, number of cycles, or another quantity that describes the service life. A generalworkflow for using RUL estimation models is: ‒ create and configure the corresponding model object;‒ train the estimation model using the available historical data; ‒ using test data of the same type as the availablehistorical data, estimate the RUL of the test component. It is also possible to use the test data recursively toupdate the model as new data becomes available, i.e. track the evolution of the RUL prediction as new databecomes available.