A New Condition Monitoring System Improves the Reliability of the RCD Element During the MPD Jobs

2021 ◽  
Author(s):  
Krzysztof Karol Machocki ◽  
Zahrah Marhoon ◽  
Amjad Shaarawi ◽  
Ossama Sehsah ◽  
Tom Dixon ◽  
...  
Keyword(s):  
System Design ◽  
Real Time ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Coolant Fluid ◽  
Factors Affecting ◽  
The Real ◽  
Wellbore Pressure ◽  
Catastrophic Failures ◽  
Condition Monitoring System

Abstract Managed pressure drilling (MPD) is a technology that allows for precise wellbore pressure control, especially in formations of uncertain geomechanical properties (in specific: Fracture pressure and pore pressure gradients). The rotating control device (RCD) is the backbone to the MPD equipment. A new condition monitoring system was developed to improve the reliability of the RCD elements and to eliminate its catastrophic failures during MPD jobs. The new method to increase the reliability of an RCD is based on understanding and quantifying the factors affecting the lifetime of the RCD components. The condition monitoring system was designed to be attached onto the RCD and collect data from an array of sensors during the MPD jobs. Sensors are measuring: vibrations, acoustic emissions, rotation, pipe movement, temperatures and contamination level in the coolant fluid. System is capable to display the measurements in the real time to the operator, giving early warnings to take actions in order to prevent catastrophic failures of the RCD during the job. Data is also recorded to allow further processing and analysis using ML and AI techniques. The authors will discuss in detail the background and rationale to the new technology, including a review of the condition monitoring system, its elements, and functionality. The system design and intended operation will be explained including, sensors and data collection points in the condition monitoring process. No catastrophic failures of the RCD were encountered when the RCD condition monitoring system was installed and running in the field up to date. The measurements collected from the array of sensors and presented in the real time to the MPD operators, allows to monitor changes in condition of the critical RCD elements. From the system design, sensor type, and frequency of data inputs, it was concluded that the quantification of some parameters affecting the lifetime of RCD could be successfully performed in post analysis, using advanced AI techniques. This condition monitoring system can optimize the MPD operations, making MPD jobs safer and reducing the Non Productive Time. The novelty of this condition monitoring system is in the approach of measuring and displaying critical values to the operator during the job and possibility to quantification of the factors affecting the RCD elements lifetime.

Online Real-Time Monitoring System Through Using Adaptive Angular-Velocity VKF Order Tracking

2012 ◽  
Author(s):  
Ting-Chi Yeh ◽  
Min-Chun Pan
Keyword(s):  
Angular Velocity ◽  
Real Time ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Theoretical Derivation ◽  
Periodic Loading ◽  
Order Tracking ◽  
Computation Scheme ◽  
Condition Monitoring System ◽  
Operation Status

When rotary machines are running, acousto-mechanical signals acquired from the machines are able to reveal their operation status and machine conditions. Mechanical systems under periodic loading due to rotary operation usually respond in measurements with a superposition of sinusoids whose frequencies are integer (or fractional integer) multiples of the reference shaft speed. In this study we built an online real-time machine condition monitoring system based on the adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm, which was implemented through a DSP chip module and a user interface coded by the LabVIEW®. This paper briefly introduces the theoretical derivation and numerical implementation of computation scheme. Experimental works justify the effectiveness of applying the developed online real-time condition monitoring system. They are the detection of startup on the fluid-induced instability, whirl, performed by using a journal-bearing rotor test rig.

scholarly journals Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 304
Author(s):  
Sakthivel Ganesan ◽  
Prince Winston David ◽  
Praveen Kumar Balachandran ◽  
Devakirubakaran Samithas
Keyword(s):  
Standard Deviation ◽  
Power Quality ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Classification Accuracy ◽  
Induction Motors ◽  
Quality Data ◽  
Starting Current ◽  
Quality Issues ◽  
Condition Monitoring System

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

Sign in / Sign up

Export Citation Format

Share Document