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Molęda, M.; Małysiak-Mrozek, B.; Ding, W.; Sunderam, V.; Mrozek, D. Maintenance Strategies in Industry. Encyclopedia. Available online: https://encyclopedia.pub/entry/46904 (accessed on 27 July 2024).
Molęda M, Małysiak-Mrozek B, Ding W, Sunderam V, Mrozek D. Maintenance Strategies in Industry. Encyclopedia. Available at: https://encyclopedia.pub/entry/46904. Accessed July 27, 2024.
Molęda, Marek, Bożena Małysiak-Mrozek, Weiping Ding, Vaidy Sunderam, Dariusz Mrozek. "Maintenance Strategies in Industry" Encyclopedia, https://encyclopedia.pub/entry/46904 (accessed July 27, 2024).
Molęda, M., Małysiak-Mrozek, B., Ding, W., Sunderam, V., & Mrozek, D. (2023, July 18). Maintenance Strategies in Industry. In Encyclopedia. https://encyclopedia.pub/entry/46904
Molęda, Marek, et al. "Maintenance Strategies in Industry." Encyclopedia. Web. 18 July, 2023.
Maintenance Strategies in Industry
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This entry is adapted from the peer-reviewed paper 10.3390/s23135970

Maintaining equipment in good condition is an important issue in the production process. Appropriate service and maintenance contribute to a high level of availability and reduce production downtimes. On the other hand, in power plants, maintenance costs represent a significant financial expense. Therefore, it is essential to achieve acceptable production results while optimizing service costs.

power industry energy production predictive maintenance (PdM)

1. Maintenance Strategies in Industry

Maintaining equipment in good condition is an important issue in the production process. Appropriate service and maintenance contribute to a high level of availability and reduce production downtimes. On the other hand, in power plants, maintenance costs represent a significant financial expense. Therefore, it is essential to achieve acceptable production results while optimizing service costs. According to the definition in the European Standard EN13306 [1], maintenance is defined as a combination of all technical, administrative and managerial actions during the life cycle of an item intended to retain it in, or restore it to, a state in which it can perform the required function. Thus, maintenance includes all activities related to inspections, condition monitoring, routine maintenance, replacement of parts, repairs, overhauls, as well as planning and supervision of all these activities.
Maintenance strategies can be classified in terms of the time when a repair is performed relative to the occurrence of a failure. There are three basic approaches of maintenance as shown in Figure 1:
Sensors 23 05970 g003 550
Figure 1. Maintenance strategies showing various moments of the repairing process before and after the potential failure of the equipment (based on [2]).
  • Corrective;
  • Preventive;
  • Predictive.

2. Corrective Maintenance

Corrective maintenance implies taking action after a failure has occurred. This approach minimizes the cost of servicing the equipment, thus extending the maintenance interval, but it comes at the expense of increased risk of equipment unavailability. The negative effects of corrective maintenance may manifest in:
  • Lost revenue, increased cost of repairing the equipment or related equipment being more damaged, which is a result of a primary failure;
  • Increased time and cost of repair—a result of unplanned downtime.
A simple real-life example of corrective maintenance is replacing a light bulb in a car. The item is only replaced when it burns out, for which the drivers are prepared by having a set of bulbs in reserve.
As stated above, this approach should be used for non-critical, easily repairable equipment. However, a more proactive approach is expected for components whose failure can cause downtime, e.g., steam boiler or turbine in a power plant. The same applies to equipment whose failure may contribute to the degradation or destruction of associated equipment, e.g., conveyor belts in explosion hazardous areas or evaporators or high-pressure steam pipelines. Depending on the procedures defining the moment when the repair should occur, actions can be taken immediately or deferred, depending on the priority and the potential consequences of the failure. In the case of continuous production, a prevalent situation is when some parts permanently work in a defective state. This is difficult to observe since the work parameters of an element slowly deteriorate (which is reflected in, e.g., reduced efficiency, increased vibration, heating). At the same time, they have no or low impact on the efficiency of production.

3. Preventive Maintenance

The purpose of preventive maintenance is to avoid unplanned downtime through scheduled periodic inspections and replacements. Typically scheduled tasks include lubrication, adjustments, oil changes or advanced diagnostics. Maintenance intervals can be planned on the basis of manufacturer recommendations, analysis of quality parameters such as MTBF (mean time between failure) and MTTF (mean time to failure). Preventive maintenance ensures good equipment condition and reduces the risk of potential downtime. However, it does not protect against unexpected failures and defects of elements not covered by the maintenance. Additionally, replacing parts too often is not always a good option, for two reasons [3]:
  • By changing an original part with a replacement, the useful life of the whole unit (machine) could be shortened due to an additional risk of failure of the part, assembly error, hidden defects or non-matching part;
  • New parts and consumables have a higher probability of being defective or failing than existing materials that are already in use.
Figure 2 shows the probability distribution of failures over the life cycle of a machine. The risk of failure is higher at start-up, then drops and increases again with wear-out. The statistically determined period between these states can be used as a determinant of the replacement period [4][5].
Sensors 23 05970 g004 550
Figure 2. “Bathtub” visualizing the probability of failures in early and late stages of the life cycle of a machine.
The disadvantage is also the necessity of planning maintenance and costs. Effective preventive maintenance planning in energy generation should align maintenance intervals with the required plant availability. For the time-based approach, the authors of works [6][7][8][9] propose a cost-reliability model to find the optimal policy by improving reliability over low cost. Planning the schedules requires historical data for analyses of maintenance history, usage conditions or a failure history. 

4. Predictive Maintenance

In predictive maintenance, the servicing is carried out when it is required, usually shortly before a fault is expected. The essence of this approach is to predict the health of a machine based on repeated analysis or known characteristics.
Commonly used conventional predictive maintenance techniques are based on periodic measurements that cover the following [3][10].

4.1. Vibration Monitoring

This technique applies to all motion and rotating machinery and is widely used in the industry for diagnostic, condition monitoring and prediction functions. Predictive techniques involve trend analysis for vibration levels, in particular frequency ranges or signal profile analysis. Trend analysis is used to determine remaining useful life and to evaluate component deterioration. Since the vibration level is itself an indicator of poor condition, predictive analyses can easily be made using only this measurement. Signal profile analysis provides the possibility to detect and classify unwanted events. Detection of characteristic signal patterns or anomalies enables discovering specific faults such as leaks, seizures or material loss. The use of this method is costly because it requires the installation of additional measurement equipment. However, developing analytical methods such as those based on machine learning allows for rapid diagnostics without involving experts in the analysis. The vibration monitoring can cover devices and their components such as pumps, fans, compressors, gearboxes, engines, turbines.

4.2. Thermography

This technique is used to predict and diagnose the condition of equipment and systems based on temperature measurements. Advanced instrumentation allows for monitoring infrared emissions using thermal imaging cameras, infrared thermometers or line scanners. The analysis of obtained results (temperature, its variations and distribution) allows determining the condition of the device and detecting potential anomalies. In practice, thermography can be used as a non-destructive method to detect wall thickness caused by corrosion and flow erosion in high-temperature pressure pipe [11] and to determine the loss of material in the boiler water–wall tubing [12]. Thermography is also applied for diagnosing electrical equipment, detecting oil leaks [13] and detecting faults in photovoltaic (PV) farms. In the latter case, it enhances the capability and safety of inspections [14][15] and provides methods to determine PV panel health [16][17].

4.3. Oil Analysis

Oil plays a vital role inside a working machine—it is responsible for lubricating, cooling, cleaning, protecting or sealing [18]. The systematic analysis of the chemistry and contamination of oil can provide indicators of the wear of machine components and lubrication quality. Systematic analysis of oil makes it possible to determine the state of wear of machine elements [19][20] and to plan preventive actions, such as changing oil or filters more effectively [21]. Investigations may include testing of viscosity, contamination, solid content, oxidation, nitration, total acid number, total base number, particle count. Examination of these properties can determine the quality of the lubricating performance, detecting leaks, corrosion or abnormal wear. Spectrography and ferrography are also complementary techniques in this area, allowing for the analysis of contaminants and additives. Using these methods, we can perform wear particle analysis to determine the types of deterioration such as rubbing wear, cutting wear, rolling fatigue and sliding wear [22][23]. Limitations of this method are the equipment cost and the difficulty in oil sampling and interpretation of results.

4.4. Motor Current Analysis

The electric motor is an integral part of most power plants. Its failures often lead to energy production outages. Therefore, it needs special attention. It is exposed to mechanical faults characteristic of rotating machinery, but a significant part of them is caused by electrical faults. Common failures include bearing failures, stator winding faults, rotor faults, insulation faults [24]. The methods used here (in addition to vibration and acoustic monitoring) cover:
  • Insulation resistance test—insulation may be damaged by high temperature or can be contaminated by humidity. The test consists of grounding the motor frame and applying DC voltage to the motor windings with a measuring device. Then, the device reads the resistance value [25].
  • Motor Current Signature Analysis—this is a technique used to analyze and monitor electrical induction motors, generators, power transformers and other electric equipment. This method uses the supply current to produce the current signature from frequency spectrum transformation. Faults in motor components produce anomalies in a magnetic field and change the mutual and self-inductance of the motor that appear in the motor supply current spectrum [26][27]. This method allows detecting faults such as [24][28]:
–
Broken Rotor Bar—a fault that can cause sparking and overheating in a motor. Examining the frequency spectrum of the stator currents can provide early fault detection [29][30].
–
Bearing Faults—faults caused by misalignment after bearing installation [31] and increased vibrations [32].
–
Eccentricity-related faults—a condition when air gap distance between the rotor and the stator is not equal [33][34].
–
Stator or Armature Faults—faults usually related to insulation failure. Shortened turns produce excessive heat in the stator coil and current imbalance [35][36].
–
Equipment wear—a degradation of parts observed in the long term. Equipment wear is also visible as changes in the current spectrum.

4.5. Visual Inspection

Online condition monitoring and predictive maintenance improvements sometimes cannot replace traditional inspection methods. To avoid undetected faults, maintenance with defined models and installed metering should be supported by engineering experience in the inspection process. The traditional process can be supported by modern technologies that enable mobility and access to information. Inspections are supported by augmented reality [37], mobile applications [38], radio-frequency identification (RFID) [39] and barcodes.

References

  1. Maintenance—Maintenance Terminology; Standard; European Committee for Standardization: Brussels, Belgium, 2010.
  2. Predictive Maintenance in Manufacturing Overview. Available online: https://docs.microsoft.com/en-us/previous-versions/azure/industry-marketing/manufacturing/predictive-maintenance-overview (accessed on 14 April 2021).
  3. Mobley, R.K. An Introduction to Predictive Maintenance; Elsevier: Amsterdam, The Netherlands, 2002.
  4. Carter, A.D. Mechanical Reliability; Macmillan International Higher Education: London, UK, 2016.
  5. Stapelberg, R.F. Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design; Springer Science & Business Media: London, UK, 2009.
  6. Lapa, C.M.F.; Pereira, C.M.N.; de Barros, M.P. A model for preventive maintenance planning by genetic algorithms based in cost and reliability. Reliab. Eng. Syst. Saf. 2006, 91, 233–240.
  7. Cassady, C.R.; Kutanoglu, E. Minimizing job tardiness using integrated preventive maintenance planning and production scheduling. IIE Trans. 2003, 35, 503–513.
  8. Cassady, C.R.; Kutanoglu, E. Integrating preventive maintenance planning and production scheduling for a single machine. IEEE Trans. Reliab. 2005, 54, 304–309.
  9. Radhakrishnan, V.; Ramasamy, M.; Zabiri, H.; Do Thanh, V.; Tahir, N.; Mukhtar, H.; Hamdi, M.; Ramli, N. Heat exchanger fouling model and preventive maintenance scheduling tool. Appl. Therm. Eng. 2007, 27, 2791–2802.
  10. Mobley, R.K.; MBB, C. Maintenance Engineering Handbook; McGraw-Hill Education: New York, NY, USA, 2014.
  11. Shen, G.; Li, T. Infrared thermography for high-temperature pressure pipe. Insight-Non Test. Cond. Monit. 2007, 49, 151–153.
  12. Cramer, K.E.; Winfree, W.P. Thermographic imaging of material loss in boiler water-wall tubing by application of scanning line source. In Proceedings of the Nondestructive Evaluation of Highways, Utilities and Pipelines IV, International Society for Optics and Photonics, Newport Beach, CA, USA, 9 June 2000; Volume 3995, pp. 600–609.
  13. Ralph, M.J. Power plant thermography—Wide range of applications. In Proceedings of the Information Proceedings, Las Vegas, NV, USA; 2004; Volume 135.
  14. Gallardo-Saavedra, S.; Hernández-Callejo, L.; Duque-Perez, O. Technological review of the instrumentation used in aerial thermographic inspection of photovoltaic plants. Renew. Sustain. Energy Rev. 2018, 93, 566–579.
  15. de Oliveira, A.K.V.; Aghaei, M.; Rüther, R. Aerial infrared thermography for low-cost and fast fault detection in utility-scale PV power plants. Sol. Energy 2020, 211, 712–724.
  16. Acciani, G.; Simione, G.; Vergura, S. Thermographic analysis of photovoltaic panels. In Proceedings of the International Conference on Renewable Energies and Power Quality (ICREPQ’10), Granada, Spain, 23–25 March 2010; pp. 23–25.
  17. Kim, D.; Youn, J.; Kim, C. Automatic fault recognition of photovoltaic modules based on statistical analysis of UAV thermography. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2017, 42, 179.
  18. Barrett, M.; Williams, K. Oil Analysis. Mater. Eval. 2012, 70, 32–40.
  19. Kalligeros, S.S. Predictive Maintenance of Hydraulic Lifts through Lubricating Oil Analysis. Machines 2014, 2, 1–12.
  20. Raposo, H.; Farinha, J.T.; Fonseca, I.; Ferreira, L.A. Condition monitoring with prediction based on diesel engine oil analysis: A case study for urban buses. In Proceedings of the Actuators; Multidisciplinary Digital Publishing Institute: Basel, Switzerland, 2019; Volume 8, p. 14.
  21. Jun, H.B.; Kiritsis, D.; Gambera, M.; Xirouchakis, P. Predictive algorithm to determine the suitable time to change automotive engine oil. Comput. Ind. Eng. 2006, 51, 671–683.
  22. Scott, D.; Westcott, V. Predictive maintenance by ferrography. Wear 1977, 44, 173–182.
  23. Dalley, R.J. An overview of ferrography and its use in maintenance. Tappi J. 1991, 74, 85–94.
  24. Nandi, S.; Toliyat, H.A.; Li, X. Condition monitoring and fault diagnosis of electrical motors—A review. IEEE Trans. Energy Convers. 2005, 20, 719–729.
  25. Lanham, C. Understanding the Tests that Are Recommended for Electric Motor Predictive Maintenance; Baker Instrument Company: Fort Collins, CO, USA, 2002.
  26. Miljković, D. Brief review of motor current signature analysis. HDKBR Info Mag. 2015, 5, 14–26.
  27. Bonaldi, E.L.; de Oliveira, L.E.d.L.; da Silva, J.G.B.; Lambert-Torresm, G.; da Silva, L.E.B. Predictive maintenance by electrical signature analysis to induction motors. In Induction Motors-Modelling and Control; IntechOpen: London, UK, 2012.
  28. Thomson, W.T.; Gilmore, R.J. Motor Current Signature Analysis To Detect Faults In Induction Motor Drives-Fundamentals, Data Interpretation, And Industrial Case Histories. In Proceedings of the 32nd turbomachinery Symposium, Houston, TX, USA, 8–11 September 2003.
  29. Granda, D.; Aguilar, W.G.; Arcos-Aviles, D.; Sotomayor, D. Broken bar diagnosis for squirrel cage induction motors using frequency analysis based on MCSA and continuous wavelet transform. Math. Comput. Appl. 2017, 22, 30.
  30. Guedidi, S.; Zouzou, S.; Laala, W.; Sahraoui, M.; Yahia, K. Broken bar fault diagnosis of induction motors using MCSA and neural network. In Proceedings of the 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives, Bologna, Italy, 5–8 September 2011; pp. 632–637.
  31. Benbouzid, M.E.H. A review of induction motors signature analysis as a medium for faults detection. IEEE Trans. Ind. Electron. 2000, 47, 984–993.
  32. Singhal, A.; Khandekar, M.A. Bearing fault detection in induction motor using motor current signature analysis. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 2013, 2, 3258–3264.
  33. Cameron, J.; Thomson, W.; Dow, A. Vibration and current monitoring for detecting airgap eccentricity in large induction motors. IET 1986, 133, 155–163.
  34. Al-Sabbagh, Q.S.; Alwan, H.E. Detection of static air-gap eccentricity in three phase induction motor by using artificial neural network (ANN). J. Eng. 2009, 15, 4176–4192.
  35. Joksimovic, G.M.; Penman, J. The detection of inter-turn short circuits in the stator windings of operating motors. IEEE Trans. Ind. Electron. 2000, 47, 1078–1084.
  36. Stavrou, A.; Sedding, H.G.; Penman, J. Current monitoring for detecting inter-turn short circuits in induction motors. IEEE Trans. Energy Convers. 2001, 16, 32–37.
  37. Eschen, H.; Kötter, T.; Rodeck, R.; Harnisch, M.; Schüppstuhl, T. Augmented and virtual reality for inspection and maintenance processes in the aviation industry. Procedia Manuf. 2018, 19, 156–163.
  38. Legner, C.; Nolte, C.; Urbach, N. Evaluating Mobile Business Applications in Service and Maintenance Processes: Results of a Quantitative-Empirical Study; AIS Electronic Library (AISeL): East Lansing, MI, USA, 2011.
  39. Lin, Y.C.; Su, Y.C.; Lo, N.H.; Cheung, W.F.; Chen, Y.P. Application of Mobile RFID-Based Safety Inspection Management at Construction Jobsite. In Radio Frequency Identification from System to Applications; IntechOpen: London, UK, 2013.
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Subjects: Engineering, Industrial
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Marek Molęda
,
Bożena Małysiak-Mrozek
,
Weiping Ding
,
Vaidy Sunderam
,
Dariusz Mrozek
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Revisions: 2 times (View History)
Update Date: 19 Jul 2023
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