Abstract:
Proposed is a performance detection method for use in detection of performance characteristics of air conditioning equipment. The method includes, for the standard operational status for the air conditioning equipment, capturing a plurality of standard operational parameters to generate performance models; and, during the actual operational status, capturing a plurality of actual operational parameters for conducting analysis based on performance models so as to determine the performance characteristics of the air conditioning equipment and send out a warning when the performance characteristics of the air conditioning equipment are determined to be abnormal. The invention improves on the detection accuracy of prior techniques and enables effective control of air conditioning equipment that eventually saves electrical power and costs as a result.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    1. Field of the Invention 
         [0002]    The present invention relates generally to a performance detection method, and more particularly, to a method for detecting the performance characteristics of air conditioning equipment with high accuracy. 
         [0003]    2. Description of Related Art 
         [0004]    In recent years, electrical power consumption and demand are greatly increasing. However, electrical power generation can easily lead to environmental pollution, for example, carbon dioxide emissions causing global warming, so it is difficult to acquire land for building electrical power plants. The development of electrical power resources and the construction of power transmission and distribution systems cannot meet the demand for ever-growing electrical power consumption. Thus, electrical power supply is often insufficient, and measures are taken to limit electrical power consumption during the peak-demand periods, including higher energy rates during peak periods. As such, ways need to be found and applied to efficiently reduce electrical power consumption to avoid electrical power supply shortages and high energy costs. 
         [0005]    According to statistics, air conditioning equipment consumes more power than most other kinds of electrical equipment. For example, in a semiconductor manufacturing fab, there is processing equipment, test equipment and/or air conditioning equipment. Generally, the air conditioning equipment (constant temperature water baths, air conditioning systems and chillers) account for roughly 27% of the total electrical power consumption of such a semiconductor manufacturing fab. Accordingly, improving the efficiency of utilization of the air conditioning helps to reduce electrical power consumption. 
         [0006]    In order to improve the efficiency of air conditioning equipment, such air conditioning equipment should be selected according to the environment in which the air conditioning equipment will be used. Further, the performance characteristics of the air conditioning equipment should be timely and accurately detected such that the use of the air conditioning equipment can be adjusted or timely repaired according to the performance characteristics, thereby reducing electrical power consumption or preventing failure of the air conditioning equipment. Generally, the current performance detection method detects the performance characteristics of the air conditioning equipment based on operational parameters (such as part load ratio) provided by manufacturers and the experience of the maintenance staff. 
         [0007]    However, air conditioning equipment is used in variable environments while the operational parameters provided by the manufacturers can only be used as a reference for a specific environment. In addition, the operational characteristics of the air conditioning equipment change with such factors as design construction, operational time, maintenance condition, chiller efficiency, or variation of peripheral equipment. Of course, the system operation of the air conditioning equipment also changes with such factors as climate, temperature, humidity and seasonal changes. 
         [0008]    Therefore, the current detection method has low accuracy and cannot timely detect the operational circumstances of air conditioning equipment such that the use of the air conditioning equipment can be timely adjusted or maintenance can be flexibly and optimally arranged. 
         [0009]    Therefore, there exists a strong need in the art for a performance detection method that can timely and accurately detect the performance characteristics of air conditioning equipment such that the use of the air conditioning equipment can be flexibly adjusted and/or maintenance for the air conditioning equipment can be arranged, thereby reducing equipment downtime, energy costs and electrical power consumption. 
       SUMMARY OF THE INVENTION 
       [0010]    Accordingly, the present invention provides a performance detection method for detecting performance characteristics of air conditioning equipment according to actual operational parameters captured during actual operational status of the air conditioning equipment, the method comprising the steps of: (1) capturing standard operational parameters for the standard operational status of the air conditioning equipment; (2) generating performance models according to the standard operational parameters of the air conditioning equipment; (3) analyzing the actual operational parameters according to the performance models so as to determine the performance characteristics of the air conditioning equipment; and (4) sending out a warning if the performance characteristics of the air conditioning equipment are determined to be abnormal, or, otherwise, capturing the actual operational parameters during the actual operational status of the air conditioning equipment and returning to step (3). 
         [0011]    In a preferred embodiment, the air conditioning equipment comprises an air conditioner, an air handling unit, a heat pump, a water cooling tower, a central air conditioning system and/or a chiller. The standard operational parameters and actual operational parameters are power rate, average power consumption, energy efficiency rate, coefficient of performance, part load ratio (PLR), electrical power consumption per refrigeration ton, cooling water inlet and outlet temperatures, cooling water flow, ice water inlet and outlet temperatures, ice water flow, refrigerant pressure and/or atmospheric humidity and temperature for both the standard operation status and actual operation status of the air conditioning equipment. 
         [0012]    In another preferred embodiment, the performance models can be trend diagrams and/or curve diagrams. In addition, the actual operational parameters can be analyzed by fall point analysis, regression analysis and/or correlation analysis. 
         [0013]    Compared with the prior art, the present invention improves the detection accuracy. Through the present invention, the performance characteristics of the air conditioning equipment can be timely and efficiently detected such that maintenance staff can flexibly adjust the use of the air conditioning equipment and/or arrange maintenance for the air conditioning equipment, thereby reducing the downtime of the air conditioning equipment and also reducing costs and electrical power consumption. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0014]      FIG. 1  shows the structure of a chiller; 
           [0015]      FIG. 2  is a flow diagram showing a performance detection method according to the present invention; 
           [0016]      FIG. 3  is a diagram of a performance model of the power rate and the part load ratio (PLR); 
           [0017]      FIG. 4  is a diagram of a performance model of the coefficient of performance (COP) and part load ratio (PLR); 
           [0018]      FIG. 5  is a diagram showing fall point analysis based on the performance model utilizing the power rate and part load ration (PLR); and 
           [0019]      FIG. 6  is a diagram showing fall point analysis based on the performance model utilizing the coefficient of performance (COP) and part load ratio (PLR). 
       
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
       [0020]    The following illustrative embodiments are provided to illustrate the disclosure of the present invention, wherein these and other advantages and effects will be apparent to those skilled in the art after reading the disclosure of this specification. 
         [0021]    Common air conditioning equipment is mainly divided into a material output system and a material cooling system. Through heat exchange between the material output system and the material cooling system, a suitable material such as ice water or chilled air is continuously output. The air conditioning equipment may be such as an air conditioner, a central air conditioning system, or a chiller. 
         [0022]      FIG. 1  shows a chiller  1 , which comprises a material output system  10  and a material cooling system  11 . 
         [0023]    As shown in the drawing, through heat exchange between the material output system  10  and the material cooling system  11  of the chiller  1 , warm water is chilled to cool water that can further be used in subsequent processes or facilities (the cool water can be, for example, used for cooling). Operational parameters such as atmospheric temperature, atmospheric relative humidity, refrigerant quality, the temperature of the warm water, refrigerant inlet temperature, refrigerant outlet temperature, and the temperature of the cool water, can be measured through related measurement/sensing devices. 
         [0024]      FIG. 2  is a flow diagram showing a performance detection method applied to the air conditioning equipment according to the present invention. 
         [0025]    First, in step S 21 , a plurality of standard operational parameters is captured and stored for the standard daily operational status of the air conditioning equipment. The air conditioning equipment can be air conditioner, a central air conditioning system, and/or a chiller, like the chiller shown in  FIG. 1 . The standard operational parameters can be the power rate, average power consumption, energy efficiency rate, coefficient of performance (COP), part load ratio (PLR), electrical power consumption per refrigeration ton, cooling water inlet and outlet temperatures, cooling water flow, ice water inlet and outlet temperature, ice water flow, refrigerant pressure and/or atmospheric humidity and temperature for the standard operation status of the air conditioning equipment. In a preferred embodiment, step S 21  can be performed periodically or continuously to capture the standard operational parameters of the air conditioning equipment through related parameter capturing devices (not shown). Further, the captured standard operational parameters can be stored. In addition, some descriptive information can be set in step S 21 . The descriptive information can comprise abnormal circumstances, causes of the abnormal circumstances, measures of correction, and/or maintenance actions. The descriptive information can further be stored, for example, as a form in a database. Then, the process goes to step S 22 . 
         [0026]    In step S 22 , performance models such as those shown in  FIGS. 3 and 4  are generated according to the standard operational parameters of the air conditioning equipment in normal operation. In particular, at least two of the standard operational parameters can be generalized to setup a performance model (for example, a mathematic model or a trend function) of the air conditioning equipment in normal operation. Preferably, the performance models can be presented as trend diagrams or curve diagrams and the performance models can further be stored. Then, the process goes to step S 23 . 
         [0027]    In step S 23 , during the actual operation of the air conditioning equipment, a plurality of actual operational parameters of the air conditioning equipment is captured. The actual operational parameters can be power rate, average power consumption, energy efficiency rate, coefficient of performance (COP), part load ratio (PLR), electrical power consumption per refrigeration ton, cooling water inlet and outlet temperatures, cooling water flow, ice water inlet and outlet temperatures, ice water flow, refrigerant pressure and/or atmospheric humidity and temperature in the actual operation status of the air conditioning equipment. Preferably, step S 23  is periodically performed up to continuously or randomly performed so as to capture the actual operational parameters in the actual operation status of the air conditioning equipment through related parameter capturing devices. Further, the captured actual operational parameters can be stored. Then, the process goes to step S 24 . 
         [0028]    In step S 24 , the captured actual operational parameters are analyzed according to the performance models so as to determine whether the performance characteristics and/or operational circumstances of the air conditioning equipment are abnormal, if the performance characteristics and/or operational circumstances of the air conditioning equipment are determined to be abnormal, the process goes to step S 25 , and otherwise, the process returns to step S 23 . 
         [0029]    According to a preferred embodiment of the present invention, the captured actual operational parameters can be analyzed through fall point analysis. The fall point analysis comprises single value fall point analysis and inter-value correlation fall point analysis. For example, the single value fall point analysis can be used to determine whether the actual operational parameters such as atmospheric temperature, atmospheric relative humidity, ice water inlet temperature, ice water outlet temperature, refrigerant inlet temperature and refrigerant outlet temperature are higher or lower compared with the standard operational parameters, wherein corresponding weight values may be determined or applied. Alternately, the inter-value correlation fall point analysis can be used to determine, for example, whether the correlations between parameters such as the atmospheric temperature and the refrigerant outlet temperature are higher or lower compared with the correlation between the atmospheric temperature and the refrigerant outlet temperature in previous similar circumstances, wherein the corresponding weight values may also be determined or applied. Tables 1 and 2 show result examples by using the above-described single value fall point analysis and inter-value correlation fall point analysis. Subsequently, whether the performance characteristics are abnormal can be determined and causes of the abnormal circumstances can be analyzed through artificial intelligence software, a genetic algorithm and/or a neural network. It should be noted that after the causes of the abnormal circumstances are determined, the causes can be introduced into a related database (not shown) for future equipment diagnosis and database diagnosis so as to prevent blunders. 
         [0000]    
       
         
               
             
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 (Single Value Fall Point Analysis) 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 Atmospheric 
                 Atmospheric 
                 Refrigerant 
                 Ice Water Inlet 
               
               
                 Temperature 
                 Relative Humidity 
                 Quality 
                 Temperature 
               
               
                   
               
               
                 Normal 
                 Normal 
                 Normal 
                 Normal 
               
               
                 (weight 0) 
                 (weight 0) 
                 (weight 0) 
                 (weight 0) 
               
               
                   
               
             
          
           
               
                   
                 Refrigerant Inlet 
                 Refrigerant Outlet 
                 Ice Water Outlet 
               
               
                   
                 Temperature 
                 Temperature 
                 Temperature 
               
               
                   
                   
               
               
                   
                 Higher 
                 Higher 
                 Normal 
               
               
                   
                 (weight + 1) 
                 (weight + 1) 
                 (weight 0) 
               
               
                   
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 (Inter-Value Correlation Fall Point Analysis) 
               
             
          
           
               
                   
                 Refrigerant Inlet 
                 Atmospheric 
               
               
                   
                 Temperature 
                 Relative Humidity 
               
               
                   
                   
               
               
                   
                 Higher (weight + 1) 
                 Normal (weight 0) 
               
               
                   
                   
               
             
          
         
       
     
         [0030]    In another preferred embodiment, step S 24  can use regression analysis, correlation analysis and/or tree analysis to analyze the captured actual operational parameters, and use artificial intelligence software, genetic algorithms and/or neural networks to determine whether the performance characteristics and/or operational circumstances of the air conditioning equipment are abnormal and the causes of the abnormal circumstances, or predict/detect abnormal circumstances that may occur so as to avoid them. The causes of the abnormal circumstances can be introduced to the related database so as to facilitate future equipment diagnosis. 
         [0031]    In step S 25 , if the performance characteristics of the air conditioning equipment and/or operational circumstances are determined to be abnormal, a warning signal or message is sent out. For example, if the performance characteristics or operational circumstances of the air conditioning equipment are determined to be worse, a warning is sent out. When the warning is sent out, preferably the cause of the abnormal circumstance is analyzed and corresponding measures are provided according to the above-described description information. In particular, the possible cause can be automatically discerned using a neural algorithm and/or genetic algorithm, thereby providing recommended measures for providing troubleshooting and/or maintenance to facilitate diagnosis, repair and maintenance by maintenance staff, as shown in Table 3. 
         [0000]    
       
         
               
               
               
             
           
               
                 TABLE 3 
               
               
                   
               
               
                   
                   
                 Recommended 
               
               
                 Possible Abnormal Circumstances 
                 Possible Causes 
                 Troubleshooting/Maintenance 
               
               
                   
               
             
             
               
                 low operational efficiency 
                 (water cooling type) pipe is 
                 (water cooling type) clean filter and 
               
               
                   
                 blocked or water flows too slow; 
                 repair or replace floating ball; examine 
               
               
                   
                 (air cooling type) dirty heat 
                 water level; clean condenser; (air cooling 
               
               
                   
                 sink or failure of cooling fans 
                 type) clean or replace heat sink 
               
               
                 high temperature of refrigerant 
                 too high atmospheric wet bulb, 
                 increase water tower, examine fan 
               
               
                   
                 fan failure or poor ventilation 
                 and clear air inlet and outlet 
               
               
                   
                 of cooling tower 
                 of water tower. 
               
               
                 high temperature of ice water 
                 improper operation of chiller 
                 examine whether chiller fails 
               
               
                 big difference in refrigerant 
                 too small flow of refrigerant 
                 Examine whether filter valve is blocked, 
               
               
                 temperature 
                   
                 and, if it is, clean the filter valve; 
               
               
                   
                   
                 examine whether the valve can be 
               
               
                   
                   
                 normally opened; clean water scale. 
               
               
                 Insufficient volume of refrigerant 
                 high pressure is too high or 
                 add refrigerant. 
               
               
                   
                 low pressure is too low 
               
               
                   
               
             
          
         
       
     
         [0032]      FIG. 3  illustrates a performance model (mathematical model) setup by generalizing the standard operational parameters (power rate and part load ratio) that are captured periodically/continuously for the standard operational status of the air conditioning equipment by related parameter capturing devices. As shown in the drawing, the performance model is a performance trend diagram with a trend function of y=0.167x 2 +0.7659x+0.0222, wherein R 2 =0.8756 represents the correlation coefficient between the y-axis (power rate) and the x-axis (part load ratio). Generally, the higher the correlation coefficient, the higher is the correlation between the y-axis (power rate) and the x-axis (part load ratio). A performance model with a correlation coefficient that is larger than 0.75 is a preferred reference value. 
         [0033]      FIG. 4  illustrates a performance model setup by generalizing the standard operational parameters (coefficient of performance and part load ratio) that are captured continuously or periodically in the standard operational status of the air conditioning equipment by related parameter capturing devices. As shown in the drawing, the performance model is a performance trend diagram, wherein, if the part load ratio is set to be 72%, the operation efficiency of the air conditioning equipment is optimal (coefficient of performance=1.775). Based on this, maintenance staff can flexibly adjust the settings for the air conditioning equipment. 
         [0034]      FIG. 5  is a diagram showing fall point analysis of the actual operational parameters based on the performance model of  FIG. 3 . Referring to step S 24  of  FIG. 2  and to  FIG. 5 , the captured actual operational parameters (measured values) fall below the trend line of the performance model, which generally means the performance characteristics and operational circumstances of the air conditioning equipment are much better. Thus, through such as neural algorithm and/or genetic algorithm, it can be determined that abnormal circumstances may occur to the performance characteristics or operational circumstances of the air conditioning equipment. Otherwise, if the actual operational parameters (measured values) fall above the trend line, it means that the performance characteristics and operational circumstances of the air conditioning equipment are worse. The correlation between the actual operational parameters and standard operational parameters can also be analyzed through regression analysis and/or correlation analysis. 
         [0035]      FIG. 6  is a diagram showing fall point analysis of the actual operational parameters based on the performance model of  FIG. 4 . Referring to step S 24  of  FIG. 2  and to  FIG. 6 , the actual operational parameters (measured values) do not fall in the optimal region shown in  FIG. 6 , which means that the performance characteristics or operational circumstances of the air conditioning equipment may be abnormal. The correlation between the actual operational parameters and standard operational parameters can also be analyzed through regression analysis and/or correlation analysis. It should be noted that the peak region of the curve is the optimum region where the performance characteristics of the air conditioning equipment are optimal. 
         [0036]    Therefore, the performance detection method of the present invention has the following advantages:
       (1) High accuracy. The detection accuracy is improved through automatic and standard detection procedures.   (2) Pre-warning. By determining whether the performance characteristics or operational circumstances of the air conditioning equipment are abnormal or whether a more serious abnormal circumstance will likely occur, the usage of the air conditioning equipment can be adjusted and maintenance can be arranged so as to save electrical power consumption and prevent failure of the air conditioning equipment.       
 
         [0039]    (3) Rapid process. The method allows maintenance staff to rapidly recognize possible causes and the method automatically provides at least a preferred measure.
       Compared with the prior art, the present invention improves the detection accuracy. In addition, through the present invention, the performance characteristics and operational circumstances of the air conditioning equipment can be timely, accurately and efficiently detected such that maintenance staff can flexibly adjust the usage/settings of the air conditioning equipment and/or arrange maintenance for the air conditioning equipment, thereby reducing the failure probability of the air conditioning equipment and reducing costs and electrical power consumption.       
 
         [0041]    The above-described descriptions of the detailed embodiments are provided to illustrate the preferred implementation according to the present invention, and are not intended to limit the scope of the present invention. Many modifications and variations completed by those with ordinary skill in the art can be made and should be considered to fall within the scope of the invention as defined by the appended claims.