I am exploring using machine learning to predict if a particular hardware component would fail within a timeframe, say 3 months. The ultimate goal is to minimize physical human inspection so that maintenance crew would always performance maintenance/servicing just in time before hardware failure.However, servicing are carried out at a fixed interval regardless of the condition of the hardware and it has practically never failed. The dataset we have is of this nature:equipment: pump1location: outdoor (or indoor)provider: providerAoperation: -   temperature (as a function of time)   moisture (as a function of time)   team (as a discreet function of time, ie teamA, teamB, teamC)Basically, we know some features of the equipment (here pump1), and we know some operating parameters (here temperature, moisture) as a function over time, and we also know which team did the servicing over time. I understand that survival analysis would be most suitable for a predictive maintenance problem but each operating parameters vary over time of study. Nothing is constant.While regression does not take into account time. As such how do we actually model it to predict its failure?Note: The above parameters are adapted from https://www.kaggle.com/ludobenistant/predictive-maintenance
Predicting hardware failures with limited data
dataset