As a pivotal component of Industry 4.0, the Industrial Internet of Things has significantly propelled the intelligent evolution of industrial systems. However, this advancement has led to increased system complexity and scale, consequently increasing the likelihood of operational failures and potential security threats. Performing an effective analysis of log information and accurately identifying system fault categories has become a substantial challenge for system administrators. To extract valuable insights from edge device logs more efficiently and ensure system security, we propose an intelligent method for system fault detection and localization. Our approach begins with an analysis of the system's source code to extract message and fault classification templates. Subsequently, real-time preprocessing of the log stream occurs, employing techniques, such as pattern matching and statistical grouping, to construct a feature vector–matrix. The detection and identification module then discerns abnormal feature vectors, using a fast classification algorithm to categorize these anomalies and determine fault types. The proposed methodology undergoes testing on our edge cloud platform. The experimental results demonstrate that the method achieves a fault detection and localization accuracy that exceeds 98%.