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@article{166488,
        author = {Mrs. Karthika P and Dr.P. Prabhusundhar },
        title = {Relation Discovery for Diagnostics Using Machine Learning Technique},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {614-619},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166488},
        abstract = {Warranty prediction is a most important task in reliability engineering. It needs to estimate the expected number of product failures in any given time period during the length of the warranty contract, Since the sensor capabilities and engineering effort available for diagnostic purposes is limited. It is, in practice, impossible to develop diagnostic algorithms capable of detecting many different kinds of faults that would be applicable to a wide range of product configurations and usage patterns. However, it is now becoming feasible to obtain and analyse on board data on products as they are being used. It makes automatic data-mining methods an attractive alternative, since they are capable of adapting themselves to specific product configurations and usage. In order to be though, useful, such methods need to be able to detect interesting relations between huge number of available signal. This method unsupervised for discovering useful relations between measured signals in a product, both during normal operations and when a fault has occurred. The interesting relationships are found in a two-step procedure. In the first step, identify a set of “good” models, by establishing a mean square error threshold over the complete data set. In the second step, estimate model parameters over time, in order to capture the dynamic behaviour of the system. use two different approaches here, the Least Absolute Shrinkage and Selection Operator method and the Recursive Least Squares filter. The usefulness of obtained relations is then evaluated using supervised learning to separate different classes of faults.},
        keywords = {Fault detection, diagnostics, Machine learning, Signal Processing, Algorithms and Reliability.},
        month = {July},
        }