Real-Time Condition-Based Maintenance of Friction Welding Tools by Generalized Fault Trees

Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds

“…In addition to quantitative analysis enabled by the calculation of TE probability, FTA allows for qualitative analysis of the minimal cut sets (MCS), which are the sets containing the minimum number of BEs to ensure the occurrence of a TE [29] (for example, the minimal cut sets for the FT shown in Figure 2 There have been several approaches for quantitative analysis of FT proposed in the literature, such as state-space models [30][31][32], simulation methods [29,33,34], algebraic approaches [35,36], and commercial software tools and model checkers such as Galileo [37], Coral [38], DFTCalc [39], and Storm [40], among others [41]. Although research effort that has been dedicated to FTA, the methodology does have a number of issues [42,43]. Typically, the TE represents the failure of a complex system, while BEs represent the failure of parts or components within the main system.…”

“…Contrary to FTA, the GFT methodology does not rely on previous expert knowledge of the system, as the BEs are generated automatically from data; in addition, the tree structure that best describes a given TE is automatically obtained through a training procedure. The definition of BEs from continuous data can be achieved by discretizing the data, using cutting-edge parameters [42,43], or using an anomaly detection technique to detect abnormal events which are then used as BEs in the GFT analysis [13]. With respect to the training process, it can be used to either minimize the error between the CDF obtained by the GFT and the real CDF of the TE for a given dataset, or to optimize the maintenance costs.…”

“…In [42], the authors proposed a method based on cutting-edge parameters to discretize the continuous sensor data into five classes based on their distributions. The same approach was explored by [43]. However, in this work, as in [13], an anomaly detection technique is used to define the BEs.…”

“…Typically, the signs of malfunctions in industrial assets are manifested by abnormally high or low values of the sensors monitoring the relevant process parameters [42,43]. Using the edge parameters to discretize the data and define BEs leads to the result that each variable generates five different BEs, increasing the complexity in training the GFT model.…”

“…The failure probability estimated by the GFT is obtained by an algebraic expression; thus, its prediction complexity is O (1), making prediction very lightweight and suitable for running on edge devices. Note that the training process is not exhaustive, as a dynamic pruning technique is used; i.e., for a tree with K BEs, only the trees that contain all the BEs of the best tree with K − 1 BEs are considered, as stated in [13,43], reducing state space explosion.…”