Current-based techniques for condition monitoring of pumps

Abstract

Pumps are the heart of many processes in industry and service sector. Electric motors are responsible for 69% of electric energy consumption in industry, with 22% of them being used for the operation of pumps. Pump faults can lead to process breakdowns and are thus related to high costs for the plant operator. Furthermore, faulty operation of pumps reduces the energy efficiency of the plant. In many cases, a time-based maintenance strategy is applied, which means that typical wear parts are replaced within defined time cycles, which comes with some drawbacks such as poor resource efficiency and high costs. Condition-based maintenance strategies - meaning that the replacement of parts is planned based on the condition of the pump - are often based on the evaluation of sensor signals like vibration or noise. However, the use of sensors also has some drawbacks, such as additional investment costs, frequent problems with the sensor mounting, and possible sensor faults. There is no widespread use of the current signal to make statements about the pump condition, although current sensors are installed in many applications anyway. As for motor fault diagnosis, different current-based techniques have demonstrated their function. Today, motor current signature analysis is used in industry, especially for the diagnosis of induction motors. In this thesis, the current-based diagnosis of typical pump-related faults in different applications is evaluated. In total, three different pump types are investigated: a wet-rotor pump, a dry-runner inline pump, and a submersible pump. The techniques used for motor fault detection are adapted for the diagnosis of pump-related faults. The results indicate that the faults clogging, impeller crack, and bearing wear, in particular, influence two frequencies in the current spectrum, which can be used as a basis for a condition-based maintenance strategy. Especially in wet-rotor pumps, these two fault indicators strongly vary depending on the hydraulic load point of the pump. With the help of a feature extraction method based on the adapted reference frame theory, this work demonstrates that the two frequencies can be analyzed in real time in a field environment. Furthermore, a concept for cloud monitoring is presented and validated with the help of a laboratory test. Additionally, it is demonstrated that the faults are visible if the starting current is evaluated in a time-frequency map, which has not been considered before in the literature on pump-related faults. In summary, the findings of this work indicate that current-based diagnosis methods can successfully detect typical faults in pumps, a fact that is of high interest for companies using these assets in their industrial processes.