## 1 Introduction
The method is independent of the system model and signal image, and provides a potential fault diagnosis scheme to improve the accuracy of fault diagnosis. Reference [17] proposed a fault diagnosis and reconstruction method for multi-level inverters using neural networks. The output phase voltage of the inverter is used as a diagnostic signal to detect the fault and its location. Reference [18] proposed a fuzzy logic-based PWM voltage source inverter induction motor drive fault detection and diagnosis method. This technique requires measuring the output current of the inverter to detect the intermittent loss of ignition pulses in the inverter power switch. Reference [19] proposed a machine learning technique for fault diagnosis of induction motors by using structured neural networks. This method can detect and isolate common fault types such as single switch open-circuit fault, back short-circuit fault, short-circuit fault and unknown fault. Reference [20] proposed a fault diagnosis strategy for cascaded H-bridge multilevel inverter systems based on principal component analysis and multi-class correlation vector machines. Reference [21] proposes an OC fault diagnosis and online monitoring scheme for grid-connected single-phase inverters using an adaptive neuro-fuzzy inference system algorithm. Reference [22] proposes OC fault diagnosis and sliding-window classification based on hybrid ensemble learning. Higher fault diagnosis accuracy is achieved and the fault diagnosis speed is improved. Reference [23] proposes a data-driven fault diagnosis method for three-phase PWM inverter in induction motor drives to realize simultaneous diagnosis of IGBT and current sensor fault of three-phase PWM inverter in induction motor drives. Reference [24] proposes a IGBT open-circuit fault diagnosis method based on transferable data driven to improve the generalization performance of fault diagnosis model. Random forests (RFs) algorithms are suitable for regression and classification, due to their excellent performance in pattern recognition, state monitoring, system control and other fields [25; 26; 27; 28; 29]. Reference [27] proposed a RFs-based fault identification model for power transformer by radio frequency identification and the feature extraction of differential measurement current data, which can effectively distinguish internal faultsand disturbances. Integrating XGBoost in RFs, a data-driven based wind turbine fault detection method is proposed in [28], which prevents over-fitting when dealing with multidimensional data. Reference [30] proposed a RFs regression based implementation of SVPWM for a two-level inverter, which can improve the performance of the three-phase induction motor (TIM) drive. Reference [31] proposed a DAWRF algorithm for IoT fault detection based on edge computing and blockchain, which improved the traditional RFs algorithm effective application in IoT fault detection. In practical applications, most data-driven methods pay great attention to the robustness and generalization ability of fault diagnosis. Reliable diagnosis needs to adapt to as many operating conditions as possible, which is not fully addressed in most literatures. In addition, data-driven algorithms often suffer from algorithmic accuracy bottlenecks and huge training burdens. Aiming at the above problems, this paper proposes a data-driven IGBT open-circuit fault diagnosis method. Its salient features and advantages are as follows:  
1. In order to extract the mapping relationship between fault features and failure modes, a robust accuracy weighted random forests algorithm is proposed, which uses out-of-bag datasets and object datasets with random disturbances to evaluate the performance of the fault diagnosis model. In accordance with the evaluation results, the weights of the decision trees are changed accordingly to improve the anti-noise ability of RFs. 2. Several hyper-parameters of the trained diagnostic model are optimized by an optimization programming framework (OPF).