I am trying to predict the failure of equipment that heats up the liquid in a pipeline using a heat exchanger. The heat exchanger gets built up inside the pipe and thus needs to be flushed every once in a while. There are sensors around the device that are collecting data like temperature, flow rate, pressure, etc. every hour. The flushing events happen once or twice a year. The recorded dates for the events are not very specific, only for the month. There is no particular measure that the operators monitor to get an idea of the health of the heat exchanger. The flushing was done neither due to the actual status nor scheduled. But the efficiency should be highest after every flushing. I thought about using anomaly detection, but that applies to equipment that runs normally most of the time and anomalies occurring infrequently. The failure is a constant and gradual process. If there is a pattern in the data it should be a gradually decreasing curve.
Another idea I had was to predict the remaining useful life method. Basically rank the time periods leading to the flushing event by how close in time, with the closest being the most severe. So it is basically a classification problem. But the thing is the flushing date is not exact which only has the month and year. There is a lot of missing values for some of the key measures which might be indicators for on and off time. Plus this is not the same problem as failure prediction because it is not failure. Even if the flushing event doesn't happen, the equipment still works. And before every time the equipment is flushed, the condition of the equipment may vary.
What is the best way to kind of quantify the deterioration rate of the equipment?