Anticipation of air conditioning failures

An early warning system was developed by researchers at the Pascual Bravo University Institution in Medellin, Colombia, using microcontrollers and low-cost sensors. Carlos Alberto Valencia Hernández, Arley Salazar Hincapie, Andres Felipe Romero Maya*

The maintenance of air conditioning systems is usually carried out based on the recommendations of the manufacturers, technical literature and existing international regulations. Such recommendations are based on team operations under ideal environmental conditions. In many cases, air conditioning and refrigeration systems are installed in corrosive environments,  with high levels of particles dissolved in the air, surrounded by high temperature sources and among other particularities that can alter the operating walls of the system.

Heating, ventilation, and air conditioning (HVAC) systems represent a significant and growing sum in energy users. The residential and commercial sector accounts for between 20 and 40% of final energy consumption in developed countries, surpassing in many cases other large sectors, such as industry and transport. Dentro del sector residencial y comercial, los sistemas HVAC representan aproximadamente el 50% del total, y tienen un equivalente al 10-20% del consumo final de energía (Perez-Lombard, Ortiz, & Maestre, 2011).

Proporcionar comodidad necesaria para los consumidores en establecimientos ocupados por personas y además propiciar las condiciones de almacenamiento para los diferentes productos en restaurantes, cafeterías requiere grandes sistemas de HVAC y grandes cantidades de energía (Zlatanović, Gligorević, Ivanović, & Rudonja, 2011). En los edificios destinados para oficinas, la fiabilidad de los sistemas de HVAC es seriamente considerada como un aspecto importante de la productividad y el confort de los ocupantes (Kwak, Takakusagi, Sohn, Fujii, & Park, 2004). In fact, inefficient operation and maintenance of HVAC systems can cause energy waste, customer complaints, poor indoor air quality, and even damage to the environment. Por lo tanto, el mantenimiento de sistemas de climatización debe ser planeado y llevado a cabo con eficacia para asegurar la satisfacción de los ocupantes con el servicio y el sistema (Au-Yong, Ali, & Ahmad, 2014). 

Prevention is better than cure, translated in industry terms, condition-based maintenance is more effective than fail maintenance (corrective maintenance). Según Betts, (2013), los sistemas de aire acondicionado y refrigeración pueden ser mantenidos aun con una  reducción de los programas de mantenimiento costosos. Companies that require frequent replacement or maintenance have negative repercussions on costs and business profitability. Por cada 1000 tonelada de capacidad de refrigeración, el consumo de energía es de 750 kW en el compresor, un adicional de £ 31.50 ($ 48 USD) por hora o £ 63,000 ($ 96,400 USD ) por año se debe a sistemas ineficientes basadas en un servicio 2000 horas (Betts, 2013). This indicates that equipment in optimal maintenance conditions has a lower operating cost, also extends the useful life of this component and maximizes energy efficiency. Por otra parte, se puede garantizar que los equipos de climatización ofrecerán un rendimiento consistente de alto nivel y alivia la necesidad de mantenimientos y garantías innecesarias (Betts, 2013).

When an air conditioning installation works correctly, it has values of pressures, electrical consumption, temperature and temperature difference, which are within normal estimated values according to the project. When an air conditioning system breaks down, it externalizes its "ailment" by changing several of these values. The installation of pressure gauges, clamp meters, thermometers and a good reasoning of the detected values, will allow the user or technician, to give a specific diagnosis of the reason for the breakdown (Buqué, 2006). It should be borne in mind that none of the components of an air conditioning system works independently, so the analysis of the operation must be carried out separately and together, in view of the fact that a breakdown may be caused by more than one cause, a breakdown located in a component or an accessory will modify the normal operation of the main parts of the system (Buqué, 2006).

Un sistema de aire acondicionado con poco o nulo mantenimiento representa hasta el 30% de la energía total consumida en los edificios comerciales (Mulumba, Afshari, Yan, Shen, & Norford, 2015). Heating, ventilation and air conditioning (HVAC) systems fail suddenly due to problems caused by improper installation, incorrect maintenance. These problems or failures include mechanical failures such as engine blockage, valve obstruction, actuator leaks, or control problems associated with sensor failure, poor sensor feedback, or incorrect control logic, there are also failures associated with the poor condition of the heat exchangers, errores de diseño y finalmente un intervención inadecuada del operador, talles fallas pasan desapercibidas durante largos periodos de tiempo hasta que el deterioro en el rendimiento se vuele lo suficientemente grande como para causa la falla del equipo y la interrupción del servicio (Schein, Bushby, Castro, & House, 2006). Generalmente, los fabricantes de equipos de aire acondicionado recomiendan las condiciones de operación, periodos y actividades necesarias para el mantenimiento (Wu et al., 2006)

Four types of maintenance programs for refrigeration and air conditioning systems stand out: 

• Testing and inspection
• Scheduled preventive maintenance
• Condition-based maintenance
• Corrective maintenance

Preventive maintenance can be divided into time-based preventive maintenance and condition-based maintenance. Time-based preventive maintenance mainly applies to non-repairable components that have a certain service life. El mantenimiento preventivo basado en la condición, también llamado mantenimiento predictivo, es aplicable a los componentes que pueden presentar una falla repentina (Kwak et al., 2004). Condition-based preventive maintenance in order to detect symptoms of a failure, is the way to reduce an emergency shutdown and corrective maintenance through the execution of preventive measures that are at the base of consecutive operation, monitoring the status of the system during a real observation time and periodic inspection por parte del personal de mantenimiento (Kwak et al., 2004).

One of the ways to ensure the reliability of an air conditioning system is through the implementation of a preventive maintenance plan.  El mantenimiento preventivo se puede dividir en mantenimiento preventivo en función del tiempo y mantenimiento basado en la condición (Kwak et al., 2004). With measuring instruments permanently installed at the most important points in an air conditioning system, through a study that allows establishing a  history of these measured values and their evolution over time, it is possible to generate alarms that indicate the poor operation of one or more components of the system.

According to the above, the Pascual Bravo University Institution developed an early warning system through the use of microcontrollers and low-cost sensors. The early warning system was generated by altering the operating variables of a Split air conditioning system with a capacity of 9000 BTU/h, a group of temperature, pressure and electric current sensors were installed in order to monitor the alteration of these values under fault conditions. Figure 1 shows an outline of the installation and the procedure  used to develop the early warning system. 

Figure 1. Basic scheme for the development of the early warning system.

Initially the values of the most relevant variables were determined during the operation of the air conditioning system in an operating time of 8 hours, the measured average values were used as a point of  comparison to determine the deviation of the variables under abnormal operating conditions.  By comparing the most relevant variables of the  system under normal and abnormal operating conditions, alarms and a range of causes associated with this failure were generated. According to the alteration of the characteristic parameters of operation, an algorithm was structured using fuzzy logic, starting from the observation of normal operating values as a differential point for the generation of alarms and recommendations. The program allowed the visualization of the alarms through a computer or an LED screen installed near the point of operation of the equipment. 

Through real-time monitoring, and varying the operating conditions of the equipment in the condensation and evaporation process, the altered parameters were determined, using the diffuse logic and the microcontroller, the corresponding alarm was generated and the possible events that could be altering the operation of the equipment outside its normal parameters. Abnormal condensation and evaporation conditions were simulated for which the system generated the following alarms according to possible events as shown in Figure 2. 

Figure 2. Generation of alarms and events.

The generation of early warnings by detecting and diagnosing faults in refrigeration and air conditioning systems using Arduino microcontrollers reduces the sudden occurrence of system failures through a low-cost system, avoiding the shutdown of equipment, economic losses in production, high levels of dissatisfaction in the end customer, overtime in maintenance tasks and the general failure of those systems that depend on air conditioning such as Racks, computer rooms, data center.
Conclusions

By identifying minor anomalies before they become major problems, the lifespan of equipment can be extended. In addition, repairs can be scheduled when convenient, reducing downtime and avoiding overtime. As an added value, the consumption of electrical energy was improved, since the operation under conditions of failure in air conditioning systems generates energy consumption above the normal values specified by the manufacturer. 

References

• Alsaleem, F., Abiprojo, R., Arensmeier, J., Hemmelgarn, G., & Louis, S. (2002). HVAC System Cloud Based Diagnostics Model * Corresponding Author, 1–9.
• Au-Yong, C. P., Ali, A. S., & Ahmad, F. (2014). Improving occupants' satisfaction with effective maintenance management of HVAC system in office buildings. Automation in Construction, 43, 31–37. http://doi.org/10.1016/j.autcon.2014.03.013
• Buqué, F. (2006). Manual Practico de Refrigeracion y Aire Acondicionado Tomo II (MARCOMBO,). Colombia: Alfaomega.
• Chandrashekaran, A., & Gopalakrishnan, B. (2008). Maintenance risk reduction for effective facilities management. Journal of Facilities Management, 6(1), 52–68. http://doi.org/10.1108/14725960810847468
• Du, Z., Fan, B., Chi, J., & Jin, X. (2014). Sensor fault detection and its efficiency analysis in air handling unit using the combined neural networks. Energy and Buildings, 72, 157–166. http://doi.org/10.1016/j.enbuild.2013.12.038
• Kwak, R.-Y., Takakusagi, A., Sohn, J.-Y., Fujii, S., & Park, B.-Y. (2004). Development of an optimal preventive maintenance model based on the reliability assessment for air-conditioning facilities in office buildings. Building and Environment, 39(10), 1141–1156. http://doi.org/10.1016/j.buildenv.2004.01.029
• Lo, C. H., Chan, P. T., Wong, Y. K., Rad, a. B., & Cheung, K. L. (2007). Fuzzy-genetic algorithm for automatic fault detection in HVAC systems. Applied Soft Computing, 7(2), 554–560. http://doi.org/10.1016/j.asoc.2006.06.003
• Mulumba, T., Afshari, A., Yan, K., Shen, W., & Norford, L. K. (2015). Robust model-based fault diagnosis for air handling units. Energy and Buildings, 86, 698–707. http://doi.org/10.1016/j.enbuild.2014.10.069
• No, F., & Proure, C. (2015). Ministry of Mines and Energy Republic of Colombia.
• Padilla, M., & Choinière, D. (2015). A combined passive-active sensor fault detection and isolation approach for air handling units. Energy and Buildings, 99, 214–219. http://doi.org/10.1016/j.enbuild.2015.04.035
• Perez-Lombard, L., Ortiz, J., & Maestre, I. R. (2011). The map of energy flow in HVAC systems. Applied Energy, 88(12), 5020–5031. http://doi.org/10.1016/j.apenergy.2011.07.003
• Schein, J., Bushby, S. T., Castro, N. S., & House, J.M. (2006). A rule-based fault detection method for air handling units. Energy and Buildings, 38(12), 1485–1492. http://doi.org/10.1016/j.enbuild.2006.04.014
• Wang, H., Chen, Y., Chan, C. W. H., Qin, J., & Wang, J. (2012). Online model-based fault detection and diagnosis strategy for VAV air handling units. Energy and Buildings, 55, 252–263. http://doi.org/10.1016/j.enbuild.2012.08.016
• Wang, L., Greenberg, S., Fiegel, J., Rubalcava, A., Earni, S., Pang, X., ... Hernandez-Maldonado, J. (2013). Monitoring-based HVAC commissioning of an existing office building for energy efficiency. Applied Energy, 102, 1382–1390. http://doi.org/10.1016/j.apenergy.2012.09.005
• West, S. R., Guo, Y., Wang, X. R., & Wall, J. (2011). AUTOMATED FAULT DETECTION AND DIAGNOSIS OF HVAC SUBSYSTEMS USING STATISTICAL MACHINE LEARNING CSIRO Energy Technology , Newcastle , Australia CSIRO ICT Centre , Sydney , Australia, 14–16.
• Wu, S., Clements-Croome, D., Fairey, V., Albany, B., Sidhu, J., Desmond, D., & Neale, K. (2006). Reliability in the Whole Life Cycle of Building Systems, 13, 1–17. http://doi.org/10.1108/09699980610659607
• Yang, H., Cho, S., Tae, C.-S., & Zaheeruddin, M. (2008). Sequential rule based algorithms for temperature sensor fault detection in air handling units. Energy Conversion and Management, 49(8), 2291–2306. http://doi.org/10.1016/j.enconman.2008.01.029
• Zlatanović, I., Gligorević, K., Ivanović, S., & Rudonja, N. (2011). Energy-saving estimation model for hypermarket HVAC systems applications. Energy and Buildings, 43(12), 3353–3359. http://doi.org/10.1016/j.enbuild.2011.08.035

Author: Duván Chaverra Agudelo

Jefe Editorial en Latin Press, Inc,.

Comunicador Social y Periodista con experiencia de más de 16 años en medios de comunicación. Apasionado por la tecnología y por esta industria. [email protected]