Research papers in secure fault diagnosis for Industrial Internet of Things (IIoT) focus on developing methods to detect, isolate, and mitigate faults in industrial systems while ensuring the security and integrity of the diagnostic process. IIoT networks integrate sensors, actuators, controllers, and machinery to enable real-time monitoring and automated decision-making, but this interconnectivity also exposes the system to cyber-physical attacks that can masquerade as faults or interfere with fault diagnosis. Researchers have explored secure fault diagnosis frameworks that combine traditional fault detection techniques with cybersecurity measures to prevent false alarms, misdiagnosis, and malicious tampering. Methods include model-based approaches, where system behavior is compared against expected operational models; data-driven techniques using machine learning and deep learning to identify anomalies; and hybrid approaches that fuse model-based and data-driven insights for improved accuracy. To ensure security, encryption, authentication, and trust management mechanisms are integrated into the diagnostic workflow to protect sensor data and communication channels from eavesdropping, spoofing, or manipulation. Edge and fog computing paradigms are leveraged to perform secure and low-latency fault analysis close to the source, reducing dependence on centralized systems and mitigating risks from network attacks. Recent works also investigate blockchain-assisted fault diagnosis to provide tamper-resistant logging, traceability, and secure sharing of diagnostic results across distributed industrial systems. Despite these advances, challenges remain in designing lightweight, real-time, and scalable secure fault diagnosis mechanisms suitable for heterogeneous IIoT environments with varying device capabilities and communication constraints. Overall, the literature highlights that secure fault diagnosis is essential for maintaining reliability, safety, and trust in IIoT-enabled industrial operations.