How to predict constant failing of equipment I am trying to predict the failing of an equipment that heat up the liquid in a pipeline using a heat exchanger. The heat exchanger gets build 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, flowrate, pressure, etc every hour. The flushing events happen once or twice a year. The recorded dates for the events are not very specific, only to 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 or scheduled. But the efficiency should be highest after every flushing. I thought about anomaly detection, but that applies to equipment running normal most of the time with anomaly occurs seldom. This failing is constant process and gradual. If there is a pattern the data it should be a gradualy decreasing curve. 

Another idea I had was to predict the remaining useful life method. Basically rank the time periods leading to flushing event by how close in time, with 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 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 deteriorate rate of the equipment?
You should try to frame as a prediction problem. Clearly define the target and then map that target to a technique:

Mean time before failure (Survival analysis)
Failure rates per million hours (Estimate Poisson distribution)
Failure within a time window (Binary classification)

Generally, Bayesian methods are used because the sparse available data can be used as a prior.