Replacements vs repairs. What is really a breakdown from the point of view of the data?
|Title||Replacements vs repairs. What is really a breakdown from the point of view of the data?|
|Summary||Finding ways to test how similar or different are the actions performed on a faulty machine. How does the effect of an intervention on the machine affect the data?|
|Keywords||Predictive maintenance, classification, regression, clustering.|
|TimeFrame||winter 2018/summer 2019|
|Prerequisites||Good knowledge of applied data science: supervised and unsupervised learning.|
|Supervisor||Pablo del Moral, Sławomir Nowaczyk|
When a machine breaks down a technician makes a diagnosis and performs some corrective action. This action can be either a repair, where no component is replaces; or a replacement is performed, where the faulty component is replaced. On top of that, the machine can also undergo preventive maintenance actions, where components are replaced even if they have not broken yet. When building a predictive maintenance system, the data scientist usually takes the replacement of a component as a failure and tends to forget about other actions that could have been performed. The validity of this assumption will be critical in determining the success of the predictive maintenance models. The goal of this project is to develop the techniques necessaries to test it, using a dataset coming from a fleet of sterilizers with partial service records where many different components are considered. Understanding the differences between repairs, replacements and preventive maintenance actions is a need to be successful in the industrial application.