# Enhancing Antifragile Performance of Manufacturing Systems through Predictive Maintenance
## 6 Method for Injection of Artificial Errors in a Virtual Environment
The risk value was denoted by an RPN (risk priority number), as one can see in Table 1. This step was performed offline, with the identification of the failure modes and assignment of the associated risk ranking determined according to a specialist's experience and recommendation. Starting from the FMEA analysis in our method, we considered, at the beginning, the same occurrence index for all of the elements with the value of 1, thus making the initial risk of the operation lower, corresponding to a proper operation (as shown in Table 1). The risk index for an individual element was computed as the maximum value between all of the RPNs associated with that element. By overlapping the risk indices over the elements represented in the FSM diagram, we can perform real-time computation of the overall risk factor as the sum of all of the possible risks of all of the linked elements, according to the state of each element. In a manufacturing line where reconfiguration is possible, these values should be computed for all possible links and set for each element through the normal behaviour of the manufacturing line. The method for introducing artificial errors involves investigating how a failure can affect the overall system operation and if it can be overcome by selecting an alternative path. It allows fast identification and classification of system weaknesses, providing a tool not only for increased plant reliability but also for better action planning. For this, we considered in the analysis both the functional dependencies between the system elements, according to the existing process models, as well as the propagation impact, according to the real-time data. Thus, depending on the error, multiple branches may be affected. The method illustrated in Figure 4 considers evaluating multiple available paths and computing as a severity factor. If the impact of this artificial error is major, alternative paths are searched in the DT model. Figure 3: Behaviour model example for Station 1. We applied the method to artificial failure injection on our manufacturing line by simulating a high fluctuation in the output of the frequency converter. This was acknowledged by setting an occurrence index for this event in Table 1 to the maximum level of 10 and recomputing the risk priority number of each element of the normal path as well as for alternative paths, if available. We did not change the occurrence index between elements where there was a dependency relationship such as an event affecting only the task completion. For elements which were in a propagation relationship with the simulated faulty element, we used a medium occurrence value determined in the offline FMEA analysis. These values and their corresponding risk factors can be recomputed during the operation according to the fault events and required maintenance procedures. As detailed in Figure 4, an alternative path can be selected, considering this event will have a lower impact on those elements. This can be achieved either if the artificial error was not applied to an element which is part of the new path or if the propagation impact is lower. Following the same approach, the method can be extended to support multiple error injections. As one can observe in Table 2, the risk index for the PLC, HMI, storage unit, and RFID sensor is represented by the maximum value between all of the possible events associated with each of these elements, as identified in Table 1. A failure of the frequency converter at Station 1 will change the occurrence value for this element to 10, thus making the new risk factor 480. As the cause can be represented by a fluctuation in the power line, this