A Fault Diagnosis Method for Multi-Condition System Based on Random Forest

The widespread use of computer technology and large-scale integrated circuits has increased the performance of the system while also significantly increasing the complexity of the system. These systems with increasingly complex structures and levels may have many different working states, and the fault features are also characterized by high dimensionality, confounding, sparseness and the like. The change of working conditions will bring about the coupling relationship between faults and faults, faults and working conditions, which will inevitably lead to problems such as long test time, difficult diagnosis and high maintenance cost. Therefore, in view of the various effects that multi-case systems may bring to diagnostic tests, research was done based on multi-case identification and random forest fault diagnosis methods. By coding the working condition information, establishing an extended decision tree, and finally establishing a random forest model, the fault diagnosis of the multi-case system is carried out. Finally, the PSpice simulation software is used to switch the case conditions and fault injection, collect and organize related the data, in turn, apply the case study to the above multi-case related research methods, and compare and analyze several methods. The results verify the effectiveness of the proposed method.