I work in renewable energy. My company gathers a lot of data from equipment. This typically includes process data (such as transformer temperature, line voltages, currents, etc.) and discrete alarms (e.g. breaker trip, inverter alarm values, transformer over temperature alarm). This is a rough example of what our data looks like (to be read as lines of csv):
- timestamp, tag, value
- 5/25/2016 14:30:01, INVERTER_1.VOLTAGE_DC, 249.5
- 5/25/2016 14:30:06, INVERTER_1.VOLTAGE_DC, 250.1
- 5/25/2016 14:45:02, TRANSFORMER_1.TEMP_ALARM, 0
- 5/25/2016 14:45:15, TRANSFORMER_1.TEMP_ALARM, 1
I'd like to start performing some pattern analysis on this data at rest, not real-time (at least for now). I believe what I'd like to attempt is unsupervised feature learning, but I'm not entirely sure. It would be nice (I think) to apply machine learning to 1) identify any patterns that aren't obvious and 2) allow an algorithm to identify signatures of patterns in the data (e.g. all inverters on a single feeder lose communications when a breaker is open).
My initial question: Is this considered time-series data? In my research so far it seems that time-series data is referencing data that is a function of time. For most of my data, as a domain expert, I don't believe that defining functions for my data is useful for this analysis. Also, in my research, it seems as though time-series data refers to real-valued values and not discrete.
Any comments or relevant references would be helpful.