Controlling the Residual Life Distribution of Parallel Unit Systems Through Workload Adjustment

Complex systems often consist of multiple units that are required to work together in parallel to satisfy a specific engineering objective. As an example, in manufacturing processes, several identical machines may need to operate together to simultaneously fabricate the same products in order to meet the high production demand. This parallel configuration is often designed with some level of redundancy to compensate for unexpected events. In this way, when only a small portion of units fail to operate due to either unexpected machine downtime or scheduled maintenance, the remaining units can still achieve the engineering objective by increasing their workloads up to the designed capacities. However, the workload of a unit apparently impacts the unit's degradation rate as well as its failure time. Specifically, this paper considers the case that a higher workload assignment accelerates the unit's degradation and vice versa. Based on this assumption, we develop a method to actively control the degradation as well as the predicted failure time of each unit by dynamically adjusting its workloads. Our goal is to prevent the overlap of unit failures within a certain time period through taking advantage of the natural redundancy of the parallel structure, which may potentially lead to a better utilization of maintenance resources as well as a consistently ensured system throughput. A numerical study is used to evaluate the performance of the proposed method under different scenarios. Note to Practitioners-Complex systems often require multiple units simultaneously working in parallel to meet a specific objective. For example, this parallel configuration usually exists in manufacturing processes, which provide some level of natural redundancy to prevent the loss of production yield due to machine failure or scheduled maintenance. However, without an active control strategy, it is not uncommon that a number of units are likely to exhibit a similar degradation path and thus lead to an overlap of unit failures. In such a case, the production requirement may not be satisfied by the remaining functional units. To prevent this incident, the paper presents a novel approach that aims at dynamically adjusting the workload assigned to each unit to actively control its degradation path and failure time. Specifically, our method is based on the assumption that the degradation rate of a unit is directly related to its workload. To implement this method, it is necessary: 1) to identify the key units that operate in the system; 2) to estimate the general relationship between the workload and the degradation rate of an individual unit using historical data and domain knowledge; 3) to understand the failure threshold and the repair time of each unit; and 4) to use online condition monitoring techniques to monitor the degradation level of each unit. With all of this information available, the proposed method predicts the future degradation level as well as the residual life of a unit under various workload assignments. Based on the predicted residual life, our method then provides a heuristic workload adjustment strategy that aims to prevent the overlap of unit failures. Our empirical results show that the proposed method consistently yields less overlap of unit failures and less loss of production than other commonly used strategies in practice, such as equal or random workload assignment strategy.