In an era described by constant technological advancement and geometric increase in population, the energy sector has become a vital and integral part of modern civilisation.
Energy generation, transmission and distribution is essential for daily domestic and industrial activities. However, this crucial sector experiences aging infrastructure, shortage of resources and socio-environmental concerns.
As the world demand for energy continues to rise, it is practical that maintaining the integrity and efficiency of energy systems become of utmost importance.
In order to preserve equipment integrity and to maintain optimal performance of energy systems and infrastructure, the energy sector has adopted predictive maintenance. The purpose of this concept is to predict the occurrence of failure of equipment or components based on repeated analysis, and to accurately estimate when and what maintenance should be performed in order to prevent the breakdown of that equipment. This shift from reactive maintenance to predictive maintenance has proven to minimize downtime, reduce cost, extend the lifetime of aging assets, enhance safety and contribute to a more sustainable future (Mobley 2002).
This article gives insights into the world of predictive maintenance of the energy sector, exploring its implications and the technology involved. In order to appreciate predictive maintenance, conventional types of maintenance must be considered.
Maintenance refers to a set of planned activities and routines put in place to avoid wear and tear, prevent deterioration and extend useful life. It is the process of preserving, repairing, and ensuring the adequate use of equipment, assets and infrastructure.
Fig 1: An overview of maintenance types
Source: Thyago P. Carvalho et al., 2019
Maintenance can be categorised into three types based on when a repair is performed in relation to the occurrence of failure. Namely:
Corrective or reactive maintenance (repairing or replacing components or the equipment after the occurrence of failure):
This kind of maintenance is done in a reactive manner, dealing with crises as they arise. Based on Keith Mobleys research, corrective maintenance typically costs three times more, than preventive maintenance. For example, one of the equipment used during the liquification of natural gas is a three-phase separator. Using the corrective maintenance approach, this equipment would not be maintained until a valve, a pipe or the equipment is damaged. Which would result in delayed productivity, avoidable cost and health hazard.
Preventive maintenance (scheduled inspections and actions to prevent breakdown)
This kind of maintenance is based on time. It involves performing maintenance before an expected failure either based on data or the manufacturers instructions. However it’s important to note that this periodic or routine maintenance doesn’t guarantee results as unexpected faults can still occur between scheduled maintenance dates.
Additionally the way equipment operates and specific variables, in each plant can also impact the lifespan of the equipment. Historical data for a water pump may not be very useful for a pump that handles a much denser fluid like heavy crude oil. Therefore, the mean-time-between-failures will vary. Leading to needless repairs or fatal disasters.
Predictive maintenance (using data analytics to predict maintenance):
This proactive maintenance approach uses data analysis and technology to monitor equipment performance. Accurately predict maintenance needs based on collected data. This type of maintenance has proven to be the most efficient because it alleviates unforeseen faults or failures that lead to emergency repairs, costly downtime, or safety hazards.
Fig 2: Flow diagram of predictive maintenance process
Source: Miguelanez-Martin and D.Flynn, 2015.
Predictive maintenance is considered to be a major part of the industry. Some predictive maintenance technique include:
Condition Monitoring Systems (CMS)
Condition monitoring involves the observation of factors, in equipment and components, such as vibration, temperature, etc. to detect any potential issues or faults. These monitoring and control systems integrate sensors and data collection methods to assess the condition of equipment.
By utilising real time monitoring and data analysis they provide up to date information, on the state of the parameters. Additionally, these systems utilise trend analysis and data patterns to identify any irregularities or anomalies. They are also equipped with programmed alarms that can alert experts when certain conditions are met.
To ensure the reliability and safety of systems traditional condition monitoring focuses on individual sensor data for each component. However advanced CMS have been implemented to enhance the systems resilience. These resilient CMS possess the capability to gather and analyze channels of information based on the health condition of each monitored variable. They also provide accurate health status assessments, for the monitored system and can quickly recover missing real time data in case of complete sensor failure (Zhang et al., 2020).
An FPSO unit refers to a floating vessel that is utilized by the oil and gas industry. Its purpose is to produce and process hydrocarbons as store oil. The LNG FPSO consists of facilities and equipment ranging from the side, to the hull side and even the subsea side (SangJe Cho et al, 2016). Among these components the compressor holds importance as it plays a crucial role in the system.
To ensure efficient gas extraction in LNG FPSO systems compressors are required. The compressor is a device that elevates gas pressure while reducing its volume. With this device, gas recovery rates can reach a 40-50% effectively doubling recovery methods effectiveness.
Any unexpected or prolonged downtime of these units greatly impacts productivity since loss of compression capability dramatically affects oil and gas production from the asset. To maintain operating levels for compressors and other components within LNG FPSOs, conditional monitoring is paramount.
Vibrational Analysis (VA)
This is an aspect of condition monitoring is crutialas it plays a role in evaluating the well being and efficiency of equipment. The method involves monitoring the vibrations in equipment which can be done from a distance, providing real time insights on the condition of the equipment as a whole.
Variations in vibration patterns can signal issues like misalignment, imbalance, wear and tear and problems with bearings. If left unattended, these changes can lead to machinery damage, avoidable downtime and personnel injury. Vibration refers to how a mechanical system moves or positions itself, over time.
Since most of the equipment in the energy sector are mechanical, VA provides the best tool, for monitoring and detecting early signs of problems(Mobley, 2002). When vibrations increase in magnitude it can be a sign that the equipment condition is deteriorating and the rate of increase indicates the extent of damage. By monitoring the vibration features associated with faults we can predict how a machine will deteriorate over time(Tse and Atherton, 1999).
VA is made up of two major steps: Vibration measurement and the interpretation of this measurement. To perform vibration analysis, vibration signals are collected by a vibration analyzer which has a sensor in the time domain. The signals are converted to frequency domain by processing FFT and the information interpreted from the signals is used to predict failure or faults(Orhan et al, 2005)
A case study carried out by Orhan, Akturk and Celik gives insight into the significance of VA as a predictive maintenance technique. This study was carried out on a pump which had nine vanes. Vibrations of the pump were closely monitored. The motor that powers the pump operates at 160Kw and 2975rpm. On the 17th of October 2001, a reference measurement was conducted (Fig.4 and 5).
Based on this measurement, it was observed that vibration patterns, in the bearing of the pump consist of multiples of the shaft rotation in the spectrum graph along with impact signals in the time domain waveform graph. These observations indicate that there is some looseness in the housing of the rotating equipment in the ball bearing. It was observed at this stage that the vibration amplitudes were relatively low. Hence no immediate maintenance was required on the pump. After three weeks, an increase in amplitude was observed.
This indicated a development of looseness in the ball bearings. As a result of this development, maintenance was carried out. On the 13th of February 2002, one of the ball bearings was removed and inspected. The inspection revealed signs of wear on the housing as corrosion, on the outer race of that ball bearing.
After the maintenance was carried out, on the 7th of march 2022, observations of the measurement showed that vibration amplitude had decreased to normal value (Fig. 8). Failure to detect the ball bearing looseness via vibration monitoring and analysis may have damaged the bearing, the pump and unnecessary downtime.
Fig 3: Overall vibration level trend of the pump inner bearing.
Oil analysis
Regularly examining the quality of lubricating oil can offer insights into the well-being of equipment and components. Alterations in the makeup of the oil can unveil problems such as contamination, wear and tear or excessive heat (Mobley, 2002).
This practice is widely employed within the energy sector to employ an approach to maintenance enabling the monitoring of equipment components such as engines, turbines, pumps and compressors. By adopting this approach, issues like wear and tear, contamination or deviations from operating conditions can be detected at an early stage (Zhu etal., 2013).
Oil analysis is a method, for identifying signs of wear in components. The process starts by examining the particles in the oil, including their size, shape and composition. This analysis provides information about the condition of moving surfaces without the need to disassemble any parts. Solid particles are generated due to friction, between the interacting components (STLE, 2021).
Oil analysis is used to monitor the condition of machines since most mechanical systems rely on oil lubrication (Bozchalooi and Liang, 2009). By analyzing the oil, we can detect any deterioration in its condition, such as the loss of additives or the presence of contamination (Sheng, 2016). The primary functions of lubrication oil are twofold. Firstly, it forms a film between moving components reducing friction and preventing them from seizing. Secondly it helps cool the components prevents corrosion on metal surfaces and keeps the system free from deposits (Sharma and Gandhi, 2008).
Changes in the physical and chemical properties of the oil can affect its effectiveness as a lubricant potentially leading to a decrease in performance (Sharma and Gandhi, 2008). Therefore, it becomes crucial to assess performance parameters to determine if the oil has degraded significantly and is no longer capable of fulfilling its intended function. There are techniques, for monitoring lubrication oil that can directly or indirectly track these characteristics (Zhu et al., 2013). Some oil analysis techniques (ASTM international, 2020) include:
– Atomic Emission Spectroscopy (AES) which identifies elements by measuring the wavelengths of light emitted when a sample is heated. It is used for wear metal analysis.
– Inductively Coupled Plasma (ICP) which quantifies metal concentrations by measuring the light emitted when a sample is ionized in a plasma.
– X-ray Fluorescence(XRF) which measures the fluorescent X-rays emitted by elements when irradiated with X-rays. It makes provision for rapid elemental analysis of oils and fluids
Motor Current Signature Analysis (MCSA)
This method entails examining the pattern of a motor to identify potential problems such, as issues with the rotor bar, mechanical imbalances and electrical malfunctions (Tavner et al, 2009). It’s important to note that MCSA detects a signal that contains current components resulting from specific rotating flux irregularities caused by faults, like rotor bar defects, mechanical imbalances and electrical issues (Thompson et al., 1999). By detecting these problems at a stage MCSA helps prevent damage and complete motor failure.
Some Applications of MCSA systems in the energy sector include:
-Pumps. These are critical components for fluid transfer and circulation. MCSA is used to monitor the condition of pump motors and detect early signs of failure or faults such as bearing wear, misalignment and electrical faults (Riffaud et al, 2017).
– Compressors are also significant components of various equipment and are essential in various energy applications. MSCA helps identify mechanical problems like unbalances and electrical issues like rotor bar defects in compressor motors. This allows for predictive maintenance to avoid costly downtime (Radosavijevic et al., 2017)
– Wind turbines. These equipment require reliable electric motors to covert wind energy into electrical power. MCSA is used to access the condition of these motors and detect issues such as gearbox wear, misalignment, or rotor imbalance. Early detection ensures efficiency, reduces avoidable downtime and maintenance cost for remote wind farms (Hameed et al., 2018).
– Electrical systems: MCSA can be employed to troubleshoot electrical systems within the energy sector. By analyzing current signatures, it helps identify electrical anomalies such as phase imbalances, harmonics, and voltage sags or surges that can affect motor performance and overall system reliability (McLoone et al., 2019)
-Infrared Thermography (IRT)
IRT, also known as thermal imaging or thermographic inspection, is a non-contact technique for capturing, analyzing and visualizing the temperature distribution of equipment and surfaces by detecting the infrared radiation they emit. This principle is based on the principles of thermodynamics. IRT relies on infrared cameras or thermal imaging devices (R.S. Adler, 2011).
These cameras are equipped with sensors, typically microbolometers, that measure the thermal energy or infrared radiation emitted by an equipment, as well as the energy reflected by the surroundings through the equipment surface (A. Kylili et al., 2014). The sensors convert the radiation into electrical signals, which are then processed to create a thermal image. The thermal image produced typically uses a color map to represent temperature variations.
Abnormal temperature patterns can indicate problems in with electrical components, bearings or other parts while the equipment is in use to avert unnecessary system downtime, catastrophic failure, and maintenance expenses (Balakrishnan et al., 1990).
IRT, also referred to as thermal imaging, is a method that captures, analyzes and visualizes the temperature distribution of surfaces and equipment by detecting the infrared radiation they emit. This technique is based on the principles of thermodynamics. IRT relies on infrared cameras or thermal imaging devices equipped with sensors (R.S. Adler, 2011).
These sensors measure the energy or infrared radiation emitted by equipment. They also detect the energy reflected by the surroundings through the surface of the equipment (A. Kylili et al., 2014). The radiation captured by these sensors is then converted into signals, which are further processed to generate a thermal image.
The resulting image typically uses a color map to represent variations in temperature. By identifying temperature patterns, potential issues with components, bearings or other parts can be detected while the equipment isin use. This proactive approach helps prevent unnecessary system downtime, catastrophic failures and reduces maintenance expenses (Balakrishnan et al., 1990).
One of the ways in which the energy sector applies IRT in recent times is by using drones fitted with infrared cameras for inspection. These drones have the ability to measure temperature, detect invisible gas and capture thermal images from a great distance. This advanced technology is applied to get a bird’s eye survey of winfarms, solar farms and oil and gas assets onshore and offshore.
Fig. 4: Schematic diagram of infrared thermography camera
Source: Young Hoon Jo et al., 2013
Ultrasonic Testing (UT)
UT is another non-contact testing method used to inspect and evaluate the integrity of equipment and components using high-frequency sound waves (Charlesworth, 2002), typically beyond the range of human hearing. UT is based on the principle of sound wave propagation through a material.
High frequency sound wave is introduced into a material, and the reflected or transmitted waves are analyzed to access the equipment’s internal structure and detects defects, leaks, electrical discharge or irregularities.
UT is applied in the energy sector for(ASTM 2021):
-Pipeline inspections. To detect corrosion, erosion and defects,
-pressure vessels and tanks inspection. To detect thinning, cracking and corrosion,
-Boiler tubes and heat exchangers. To detect wall thinning and ensure efficient and safe operation,
– Steam blade turbines. To detect cracks and material degradation
– Reactor pressure vessel, welds and reactor coolant piping inspection. To detect defects and ensure structural integrity of the reactor.
Some UT techniques are:
-Conventional ultrasonic testing: This uses standard ultrasonic probes and equipment to inspect equipment and assets,
-Phased Array Ultrasonic Testing (PAUT): This uses an array of transducer elements to steer and focus ultrasonic beans electronically. This technique provides enhanced defect detection and sizing capabilities, and is suitable for complex geometries, equipment and components (ASME, 2021).
-Time-of-Flight Diffraction (TOFD): This is used to detect and size the defect based on diffracted waves. It is highly sensitive to crack-like defects and is often used in the inspection of weld and critical components (ASME 2019).
-Full Matrix Capture (FMC) and Total Focusing Method (TFM): These provide high resolution images, localization of defects, and enhance the accuracy of defecting sizing in materials with complex structures like turbine blades (Wilcox, 2013)
-Guided Wave Ultrasonic Testing (GWUT): This uses low-frequency guided waves to inspect long section pipelines and structures. It can also detect corrosion, pitting and wall thinning of buried or insulated equipment (Datta and Shah, 2014)
Acoustic Emission Analysis (AE)
AE analysis is a non-contact testing technique adopted to monitor and assess the structural integrity of various components. This technique involves monitoring the release of ultrasonic waves or acoustic emissions generated by releasing energy from materials due to deformation, crack propagation or other stress related phenomena. It is particularly useful for detecting structural defects and monitoring pressure vessels(Kaiser, 2003).
AE analysis is a valuable tool in the energy sector for early detection of defects, structural monitoring, and ensuring the safety and reliability of critical components. By identifying stress-related faults before they lead to catastrophic failures(Yassin, 2020), this technique contributes to cost savings and increased operational efficiency in energy-related applications.
Applications of AE analysis in the energy sector (Duff, 2007):
-Pressure vessel and tank inspection. To monitor and detect cracks, corrosion or defects.
-Pipe inspection: To monitor stress corrosion cracks and other defects that can result to leaks and failure
-Weld inspection: To detect weld related defects and ensure structural integrity.
-Structural hezlth monitoring: To monitor structural components and equipment like steam pipes, generators and turbines for faults that may affect performance and safety.
Fig.5: Systematic diagram of acoustic emission.
Source: Baihui Renet al., Fracture Characteristics of Polyurethane Grouting Materials Based on Acoustic Emission Monitoring, 2021.
Role of predictive maintenance in operational cost reduction:
According to ATS, predictive maintenance can yield cost savings between 8% to 12% over preventive maintenance, and up to 40% over reactive maintenance. Although the cost of installing predictive maintenance systems is significantly higher than preventive maintenance, the long and short-term benefits outweigh preventive and corrective maintenance strategies.
Some areas of predictive maintenance reduces operational cost include:
-Predictive maintenance allows experts plan and prepare for a repair. Such planning could include shifting production to a back up equipment while repairs take place or carring out repairs during low production season. This approach alleviates unplanned downtime, unplanned outages, and cuts production losses significantly(Nunes et al., 2023)
-Predictive maintenance extends the remaining useful life of equipment and components. By detecting early signs of faults or degradation and timely intervention, equipment are deterred from reaching irreparable damage. This reduces the need to frequently replace equipment (Moubray, 1997).
-Predictive maintenance reduces the cost of maintenance by avoiding emergency repairs which can be cost intensive (Smith, 2017).
– Predictive maintenance optimizes maintenance resources by alleviating routine and time-based maintenance. This allows experts focus on equipment or components truly in need of repairs instead of following a schedule which has the potential to waste resources (Jardine et al., 2013).
– Predictive maintenance ensures energy efficiency by maintaining equipment operational levels at optimum condition. This reduces energy consumption and related operational costs (Yang and Li, 2014).
Safety Implications of predictive maintenance
Predictive maintenance has significant implications in the energy sector that encompasses improved operations and personnel safety, and reduced occurrence of accidents. Some safety implications of predictive maintenance are discussed below:
-Predictive maintenance minimizes workers exposure to hazard by reducing the need for emergency or reactive repairs. Instead, maintenance can be planned and carried out under safer conditions (Brown and Courtney, 2003).
-Predictive maintenance helps organizations enhance equipment issues that may compromise safety features. For example, sensors that detect overheating, pressure buildup or gas leakage can be critical in preventing safety hazards for personnel and equipment (Baraldi et al., 2008).
-Predictive maintenance prevents catastrophic failure by identifying potential equipment failure before they escalate into dangerous situations (Bongers, 2001).
-Predictive maintenance improves incident response by generating data used to enhance incident response plan (Rausand and Hoylan, 2004).
-Predictive maintenance data can be used to enhance worker safety training and create awareness by providing insights into specific risks associated with certain component, equipment or processes (Szymberski, 2011).
-Predictive maintenance minimizes environmental risk by ensuring optimal operation of components and equipment and reducing the likely hood of environmental incidents like spills or emissions of pollutants (Pritchard and Fox, 2017).
Predictive maintenance is heavily reliant on data collection and accurate analysis. To carry out Predictive maintenance as discussed above, temperature, pressure, level, vibration, flow and other types of variable sensors are strategically placed on equipment throughout an infrastructure, based on the type and function of each equipment.
These sensors continuously collect real-time data which is further processed and analyzed by advanced algorithms to detect patterns and anomalies that could be potentially dangerous or lead to unnecessary downtime. In more advanced predictive maintenance techniques, machine learning models predict when specific maintenance activities would be required.
By implementing predictive maintenance, companies can optimize the Remaining Useful Life (RUL) of equipment and obtain optimal system level performance(Migelanez-Martin and Flynn, 2015). Costly repairs and unnecessary downtime are reduced drastically, which would result in better allocation of resources.
Predictive maintenance is knowledge intensive and predominantly carried out or supervised by human experts. Therefore, workers in the energy sector should be trained to use thetechniquesstated in this article effectively. In addition to the training, it is also important to note that accurate collection and analysis of data is critical, as any inaccuracy in either of the processes can cause false alarms or missed opportunities for maintenance.
Predictive maintenance is reshaping the energy sector by ushering an era of proactive and data-driven maintenance practices. Harnessing the power of data analytics better equips the energy sector to meet the burgeoning demands for reliable, sustainable, and efficient energy generation and distribution.
Engr. Okoye is an Oil and Gas professional with 25 years of experience in Wells drilling, Completions, Workover and Rigless operations. He works with one of the global oi and gas giants in [email protected]
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