19 research outputs found
An algorithm for data-driven prognostics based on statistical analysis of condition monitoring data on a fleet level
No full textThe availability of condition monitoring data for large sets of homogeneous products (in the following referred as a fleet) motivates the development of new data-driven prognostic algorithms. In this paper, an intuitive and an innovative data-driven algorithm to predict the health and, consequently, the Residual Useful Lifetime (RUL) of a product are proposed. The algorithm is based on the extraction and exploitation of knowledge at a fleet level. The fleet-specific usage and the degradation profile are extracted by statistically analyzing the condition monitoring data of all the products that's belongs to the fleet. The extracted knowledge, in terms of statistical distribution of health condition and sampling time, is then exploited to predict the health and RUL of a product in the fleet. The algorithm described in this paper is able to predict the RUL of a product with a good credibility even for observation window lengths that are smaller compared to the lifetime of the product
A comparative study on data-driven prognostic approaches using fleet knowledge
No full textRecently, Prognostics and Heath Management techniques have been deeply investigated with the aim to reduce life-cycle cost of products and systems. The increasing availability of condition monitoring data in substantial quantities for multitudes of homogeneous products and the need for generic algorithms that are applicable to complex systems motivates the development of new data-driven prognostic approaches. In this paper, two data-driven algorithms, one based on a statistical approach and another based on Neural Network, are discussed and tested for an application case. The analysis of the results has shown that both the considered approaches are characterized by reliable prediction performances on Remaining Useful Life calculation, thus resulting as potential tools for the application of a Condition-Based Maintenance strategy
Short-Term Scheduling of Multi-Product Batch Plants with Sequence-Dependent Changeovers Using Timed Automata Models
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Efficient Scheduling of Batch Plants Using Reachability Tree Search for Timed Automata with Lower Bound Computations
No full textAn Intuitive and Efficient Approach to Process Scheduling with Sequence-Dependent Changeovers Using Timed Automata Models
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Modeling and Solving Batch Scheduling Problems with Various Storage Policies and Operational Policies using Timed Automata
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