Fault Prognosis System on Face-Mask Body Machine with Adabelief-Backpropagation Neural Network

Abstract

Ultrasonic welding workload on vertical roller welding components on face-mask body machine in making masks has a highvibration of up to 20kHz. This high vibration causes the locking bolt to loosen the serrations, thus causing a greater failure of function, such as wear and tear on the teeth. If this function fails, it will cause downtime and high costs in the waiting process for the cleat component to be remanufactured. The damage prognosis system based on the condition of this machine implements a Classification of types of damage along with recommendations for maintenance activities that need to be carried out on the Face-Mask Body Machine. Classification of the type of damage to this system using There is a Belief-Backpropagation Neural Network (BPNN), a method for looking for weight settings on a neural network based on the error rate obtained in the previous iteration. This method is optimized using AdaBelief, which can adapt the step size based on the "confidence" of the previous gradient to get Convergence rates and generalization abilities better so that these types of problems can be known from the vibration signal of the machine which previously the signal was parsed using wavelet packet decomposition into frequency bands to obtain component data with low or high frequency. From the results of system performance testing, the modeling accuracy is 98.4%, so this system can be declared good and feasible to use in slack detection of vertical roller welding cleat fixing bolts.