Detecting expected patterns in repeated measurements I am new to signal processing and I am trying to learn myself. To understand what i am interested in, let me show this on example (note that this is just a random example to illustrate). Here is some signal detected from a device:



You can notice there are 3 peaks there, and let's say there is a task to detect times of beginning of each peak (where it starts to rise). I then need to evaluate times of these 3 peaks and compare it to expected value. Now I am interested in ways how to detect these "patterns" and I am trying to find more info/learn about (something general, not specifically to this signal). 

So far I learnt how to use basic filters to get rid of noise (moving average, exponential smoothing) and some really simple techniques how to find some of these points (checking sudden changes (difference between two successive filtered values) in the filtered value that is above certain threshold, etc...). My question is, where can I get to know more about this area, if you can point me to some resources, direction... Something that is not about finding unknown patterns in signal, but more about being able to accurately detect already known/expected "patterns". I will be thankful for any info/references.
So far I learnt how to use basic filters to get rid of noise (moving average, exponential smoothing) 


Those are all low-pass filters! 


  checking sudden changes (difference between two successive filtered values) 


That is a high pass filter !

To be honest, I don't see "patterns" in your signal, the peaks are all pretty different, but you don't care for "patterns", either: 

You really are just interested in the signal that first passes through a low-pass filter, and then a high-pass filter.

Combined, that is a band-pass filter.

So if I'd recommend something: Look into filtering; understand the spectrum of signals, and understand how to build filters that extract specific signals from the original signal.