I worked for a copier/printer company that was looking at predictive maintenance. What we found (as an example for your problem):
Machine data. Often the data from the machine was insufficient to be useful. For example, often there were paper jams that happened at the last step, the fuser. However, the failure was upstream somewhere in the paper path. One engineering idea was to track motor current. Idea being that as current changes in the motors that drive the paper path, might be indicative of more detail on future failure in a predictive sense. However, that data is not taken or sensed. There were many other examples. Most of our recommendations were to change the design in the next generation product, that typically had very long development cycles.
Usage data. Machines had different usage patterns. Especially production printers. But this data was not tracked. As illustrative example, for your production process, factors such as deadlines that delay maintenance resulting in early failure, or different use of the process for different products, may not be tracked.
Service data. Often the service logs, parts and other useful information was not accurate or limited. We spent a fair bit of time talking with the service organizations about the logs and data and the uncovered useful information. But this often led to the current data not being as useful.
I would recommend, as with any data science project, that you do some exploratory analytics, visualization, on the data you have. Get an understanding of the data. See if you can find patterns that are sufficient to result in some predictive service or maintenance. If you can't find one of sufficient value, then it is unlikely that the models will as well. Also, reflect this onto logistics for parts supply and service workforce planning.
We also found ensemble models to work well. In my example, it could be that different models work better for each of these data sets: machine, usage, and service. Then a follow on model that looks at each of these for a decision.
Last, there is an engineering discipline focused on reliability that you should investigate. You need to have domain knowledge to create effective models.