This work investigates fault detection methods based on Artificial Immune Systems, specifically the negative selection algorithm (NSA) and other self/nonself recognition techniques. First, there was proposed a representation scheme based on hyperspheres with variable center and radius, and three models, which are very capable to generate detectors, based on that representation scheme, in an effective way. The first model employs Genetic Algorithms where each chromosome gene represents an index to a point in a quasi- random distribution, that will serve as a detector center, a decoder function will be responsible to determine the appropriate radius. The chromosome fitness is given by a valuation of the covered volume, which is calculated through a Monte Carlo integral. The second model uses the Quadtree space partition technique to generate the detectors positions and their radius. The space partition could be done by using a detection function or by recursive divisions of an initial detector that occupies the whole space. In third model, inspired on immune networks, the B cells represent the detectors and the network that is established by them gives the location and radius of each detector. Experiments with syntetic and real data show that the proposed algorithms improve scalability and perform better in fault detection.