In most potential industrial applications of artificial immune systems for early fault detection, some form of simple fault detection system already exists. We propose that this existing layer of simple, generally rule-based fault detection is analogous to innate immunity in the natural immune system. We argue that the artificial acquired immune system should focus on the detection of fault conditions not already covered by the innate immune system, most importantly on the very early symptoms of faults which, we believe, are often very similar to self. This has implications for detector generation algorithms. We test two novel detector generation algorithms that address this issue, using temperature data from refrigerated cabinets in UK supermarkets. Results show that location of detectors in problem space is important and that detector sets concentrated close to self in problem space are better at detecting early stages of fault.