The rapid integration of the Internet of Things (IoT) into high-end equipment has dramatically transformed fault diagnosis processes, enabling real-time monitoring and predictive maintenance. This technological advancement, while enhancing operational efficiency, has also introduced critical security vulnerabilities, particularly within supply chains susceptible to cyberattacks. This paper investigates the intersection of IoT security and fault diagnosis in high-end equipment, proposing a systematic secure software engineering framework. Through an extensive literature review and a detailed analysis of existing datasets on supply chain attacks, an innovative secure software engineering approach is developed. The methodology is validated through a case study on IoT-Enabled Fault Diagnosis in a High-End Car Manufacturing Supply Chain, showcasing the practical application of the proposed framework. Business Process Modelling and Notation (BPMN) simulations of the car manufacturing process reveal that 1,000 cars can be assembled and repaired in 467.33 minutes (approximately 7.79 hours or onethird of a day) using this approach. Additionally, the study introduces key metrics for process quality, data quality, and predictive model accuracy, offering a quantitative assessment of the framework's effectiveness. The findings emphasize the critical need for robust security measures in IoT-enabled fault diagnosis systems to mitigate potential risks and ensure operational integrity.