This thesis project investigates approaches for malfunction prediction using unsupervised, self-organized models, with an orientation on bus fleets. Certain bus malfunctions are not predictable with conventional methods and preventive replacements are too costly and time consuming. Malfunctions that could result in interruption of service or on degradation of safety  are of high priority to predict.The settings of the desired application define the following constraints: definition of a model by an expert is not desirable as it is not a scalable solution, ambient conditions or usage schedule must not affect the prediction, data communication between the systems is limited so data must be compressed with relevant to the problem features. In this work, definition of normal or faulty operation is not handled by an expert, but using the Wisdom of the crowd idea and Consensus Self-organized models for fault detection (COSMO), or by the system's past state by monitoring an autoencoder's reconstruction error. In COSMO each system constructs a model describing its condition and then all distances between models are estimated to find the Most Central Pattern (MCP), which is considered the normal state of the system. The measure of deviation is the tendency of a system's model to be farther from the MCP after a sequence of observations, expressed as a probability that the deviation is incidental.  Factors that apply to the total of systems, such as the weather conditions are thus minimized.The algorithms approach the problem from the scopes of: linear and non linear relations between signals, distribution of values of a single signal, spectrum information of a single signal. This is achieved by constructing relevant models of each observed system (bus). The performance of the implemented algorithms is investigated using ROC curves and real bus fleet data, targeting at predicting a set of malfunctions of the air pressure system.More tests are performed using artificial data with injected malfunctions, to evaluate the performance of the methods. By applying the method on artificial data, the ability of different methods to detect different malfunctions is exhibited.