Towards a Practical, Generally-Applicable Condition-Based Maintenance CBM System for Mud Pumps

Abstract

Unexpected failure of mud pumps during drilling operations can result in non-productive time (NPT) and increase well construction cost. Several prior studies and implementations of condition-based maintenance (CBM) systems for mud pumps have failed to provide a generalized solution for the variety of pump types encountered in the field, in particular by failing to detect damage early enough to mitigate NPT. Our research is aimed at improving upon this situation by developing a practical, generally-applicable CBM system for mud pumps. In the study reported here, a laboratory test bed with a triplex mud pump was used to collect data to test a new approach to mud pump CBM. Artificial damage was introduced to the two most frequently replaced parts of the pump, i.e., the valve and piston. An accelerometer and an acoustic emission (AE) sensor were used to collect experimental data. Based on this data, an anomaly detection algorithm was constructed using a one-class support vector machine (OC-SVM) to pin-point the early onset of mud pump failure. The CBM methodology thus developed does not require prior knowledge (data) of the mud pump itself or of the failures of its components. This is key to it being more widely deployable. The trained machine-learning algorithm in the test setup provided an accuracy greater than 90% in detecting the damaged state of the valve and piston. Only the characterization of the normal (i.e., non-damaged) state data was required to train the model. This is a very important result, because it implies that the sensors can be deployed directly onto mud pumps in the field – and additionally, that the first few hours of operation are sufficient to benchmark normal operating conditions. Also, it was observed that a multi-sensor approach improved the accuracy of detection of both the valve and piston damage. The system is able to detect early-stage damage by combining the cumulative sum control chart (CUSUM) with the damage index developed in this project. This work is the first attempt at applying semi-supervised learning for CBM of mud pumps. The approach is applicable for field use with very little or no prior damage data, and in various working conditions. Additionally, the system can be universally deployed on any triplex pump and efficiently uses the data collected in the first few hours of operation as a baseline. Consequently, the practicality and scalability of the system are high. It is expected to enable the timely maintenance of critical rig equipment before catastrophic damage, failure and associated downtime occurs. The system has been deemed promising enough to be field-trialed, and is currently being trialed on rigs in North America.