WaveNet based Autoencoder Model: Vibration Analysis on Centrifugal Pump for Degradation Estimation.

Abstract

Centrifugal pumps are versatile and have been used in a wide range of applications such as agricultural services, wastewater services, and other industrial services. The mechanism behind the pump is converting rotational kinetic energy to induce flow or raise pressure of liquid. Boiler feedewater pump (BFP) is an important piece of equipment in a thermal power generation plant. Generally, the cost of the pump itself only account less than 20% of its life cost and about 30% - 35% of the life cost spend on pump operation and maintenance. Therefore, it is important to understand the degradation status of the pumping system for optimizing the operational procedures and maintenance schedule to reduce the cost. Traditionally, engineers evaluate the performance and/or find faults by observing the vibrational signal on the pump, specifically, looking at the power spectrum density of the vibrational signal measured on different locations of the pump. However, such vibration analysis requires substantial domain knowledge and experience to accommodate all the variables caused by various conditions like different models, sizes in different plants, units and facilities. Often Vibration Analyst have to bin the vibration signal according to a predetermined frequency bins and potentially removing useful markers about vibration health.

This paper presents a novel way of conducting vibration analysis on pumps to determine the degradation trend, without requiring expert domain knowledge by extracting useful information using a WaveNet based autoencoder on the historical vibration data. WaveNet is known for processing raw audio data and building generative models. Unlike recurrent neural network (RNN), WaveNet is capable of handling much longer sequential data, which is very suitable for high frequency signals like sound and vibration signals. The autoencoder model extract essential information for reconstructing the input data. The embeddings from the autoencoders can represent the characteristics of the input data. Combining the two techniques, we were able to compress the vibration data 12x and extract the embeddings from raw vibration data and use them to estimate the degradation status of pumps. We pre-selected a collection of vibration data from pumps under “normal” condition.  The degradation trend is estimated by computing the distance of the embeddings from “normal” data to new inputs. Such model provides additional information on pump condition vis-a-vis vibration data with no prior domain knowledge. This technique can assist decision making and reduce costs from improper operation and maintenance.