Display omitted •Process data is generated by a dynamic, closed-loop milling circuit simulation.•Data-based methods for fault detection and root cause identification are evaluated.•An approach to quantify economic benefit for early fault diagnosis is presented.•The approach is suggested for robust assessment of benefit in industrial processes.•The dynamic simulation and data have been shared online to promote further analysis. The early detection and root cause identification of abnormal events in industrial processes is important, to allow for timely corrective actions, ensuring continued economic operation. This paper investigates the application of statistical fault detection methods, in conjunction with process topology data-driven techniques for root cause analysis, to a simulated milling circuit. Two faults (faulty particle size analyser and rapid mill liner wear) were simulated, and the statistical monitoring techniques tested. Fault detection proved accurate, and variables closely associated with the faults were identified by the root cause analysis. The need to further formalise the selection of data for process topology generation for root cause analysis was highlighted. The milling circuit simulation and fault data has been made available as a resource for future research. Economic performance factors were developed to quantify the impact of the faults and motivate for fault detection and diagnosis.