Fault diagnosis method for water-cooling precise air conditioning unit

Abstract

The invention relates to the technical field of air conditioning unit fault diagnosis, in particular to a water-cooling precise air conditioning unit fault diagnosis method. When the method is applied, multi-dimensional diagnosis can be carried out, and electric, mechanical and thermodynamic system faults are covered in combination with compression working conditions, compressor vibration and evaporation and condensation data. Misinformation is eliminated through time relevance, and the diagnosis reliability is improved. Moreover, after the correlation quantities are calculated, the correlation among the faults can be quantitatively analyzed, different levels of fault diagnosis evaluation can be carried out on different correlation quantities, signals with high correlation quantities can be focused and monitored, and higher priorities and more computing power can be provided for monitoring of the signals with the high correlation quantities. According to the method, the fault detection rate and diagnosis efficiency of the precise air conditioning unit are remarkably improved, relevance evaluation is carried out on all the faults, and multi-dimensional diagnosis evaluation can be carried out on the faults.

Description

Technical Field

[0001] This application relates to the technical field of air conditioner unit fault diagnosis, and particularly to a fault diagnosis method for water-cooled precision air conditioner units. Background Art

[0002] Traditional air conditioner fault diagnosis methods mainly rely on single-sensor data for threshold judgment, and it is difficult to meet the fault diagnosis requirements under complex working conditions. Since all components in the precision air conditioner system are interrelated during operation, faults in core components such as compressors, evaporators, and condensers often have coupling. However, the existing technology lacks the collaborative analysis of multi-source data and the assessment of fault relevance, resulting in problems such as high false alarm rates, inaccurate fault location, and lagging maintenance response. Especially in scenarios such as data centers where strict requirements are imposed on temperature control stability, traditional methods are difficult to detect early latent faults in a timely manner, and it is difficult to provide an effective risk level assessment for operation and maintenance decisions, seriously affecting the reliable operation and maintenance efficiency of the air conditioner system. In the fault diagnosis of water-cooled precision air conditioner units, how to solve the problems of overly single diagnostic results and lack of correlation detection between different signals in traditional detection methods is a technical problem that needs to be solved in this field. Summary of the Invention

[0003] In view of this, this application provides a fault diagnosis method for water-cooled precision air conditioner units, which can solve the problems of overly single diagnostic results and lack of correlation detection between different signals in traditional detection methods.

[0004] In a first aspect, a fault diagnosis method for a water-cooled precision air conditioner unit provided by this application includes: obtaining the compression working condition of a compressor; comparing the compression working condition with a first standard working condition to generate a compression anomaly signal; obtaining the vibration data of the compressor; generating a corresponding mechanical fault signal according to the vibration data and a second standard working condition; obtaining the evaporation and condensation working conditions of an evaporator and a condenser; generating a corresponding evaporation and condensation prompt signal according to the evaporation and condensation working conditions and a third standard working condition; retrieving all the compression anomaly signals, the mechanical fault signals, and the evaporation and condensation prompt signals according to the current moment and backtracking a historical preset duration; comparing the occurrence times of the compression anomaly signals, the mechanical fault signals, and the evaporation and condensation prompt signals, and calculating the occurrence correlation amount between the signals; and generating signal risk assessment data according to the occurrence correlation amount.

[0005] In combination with the first aspect, in a possible implementation manner, the obtaining of the compression condition of the compressor includes: obtaining the compressor discharge port pressure and the compressor suction port pressure; and obtaining a detected compression ratio according to the compressor discharge port pressure and the compressor suction port pressure; the comparing of the compression condition and the first standard condition to generate a compression anomaly signal includes: obtaining the designed compression ratio of the compressor of the air conditioner unit; and if the difference between the detected compression ratio and the designed compression ratio is greater than a preset compression difference, generating a compression ratio anomaly signal.

[0006] In combination with the first aspect, in a possible implementation manner, the obtaining of the compression condition of the compressor includes: obtaining the compressor suction port pressure and the compressor suction port temperature; retrieving a corresponding saturation temperature according to the compressor suction port pressure; the comparing of the compression condition and the first standard condition to generate a compression anomaly signal includes: obtaining a reference temperature according to the saturation temperature and a correction temperature; if the compressor suction port temperature is less than the reference temperature, generating a liquid slugging risk signal.

[0007] In combination with the first aspect, in a possible implementation manner, the obtaining of the compression condition of the compressor includes: obtaining the actual power of the compressor and monitoring the compressor speed in real time; the comparing of the compression condition and the first standard condition to generate a compression anomaly signal includes: if the actual power exceeds a preset amplitude of the rated power, generating a power anomaly signal; comparing the compressor speed and the rated speed, and if the fluctuation of the compressor speed relative to the rated speed exceeds a preset fluctuation threshold, generating a speed anomaly signal.

[0008] In combination with the first aspect, in a possible implementation manner, the generating of a corresponding mechanical fault signal according to the vibration data and the second standard condition includes: converting the vibration data of the time domain signal into frequency domain data; extracting a first frequency spectrum at 1.5 times the rotational frequency and a second frequency spectrum at 3 times the rotational frequency according to the frequency domain data; if the peak value of the first frequency spectrum is greater than a first preset peak value, generating a bearing inner race fault signal; and if the peak value of the second frequency spectrum is greater than a second preset peak value, generating a roller fault signal.

[0009] In combination with the first aspect, in a possible implementation manner, the obtaining of the evaporation and condensation conditions of the evaporator and the condenser includes: obtaining the evaporator outlet temperature, the evaporator pipeline pressure, the condenser outlet temperature, and the condenser pipeline pressure; the generating of the corresponding evaporation and condensation prompt signal according to the evaporation and condensation conditions and the third standard condition includes: retrieving the evaporator saturation temperature corresponding to the evaporator pipeline pressure, and retrieving the condenser saturation temperature corresponding to the condenser pipeline pressure; obtaining the evaporator superheat degree according to the evaporator outlet temperature and the evaporator saturation temperature; obtaining the condenser subcooling degree according to the condenser outlet temperature and the condenser saturation temperature; if the evaporator superheat degree is greater than the first superheat degree and the evaporator pipeline pressure is lower than the first pressure standard value, generating a refrigerant shortage signal; if the condenser subcooling degree is less than the second superheat degree and the condenser pipeline pressure is higher than the second pressure standard value, generating a refrigerant excess signal; and if the evaporator superheat degree, the evaporator pipeline pressure, the condenser subcooling degree, and the condenser pipeline pressure are all within the threshold fluctuation range, if the evaporator superheat degree is less than or equal to the first superheat degree and the condenser subcooling degree is greater than or equal to the second superheat degree, generating a normal charging signal.

[0010] In combination with the first aspect, in a possible implementation manner, the obtaining of the evaporation and condensation conditions of the evaporator and the condenser includes: obtaining the condenser inlet temperature and the condenser outlet temperature; the generating of the corresponding evaporation and condensation prompt signal according to the evaporation and condensation conditions and the third standard condition includes: calculating the temperature difference between the condenser inlet and outlet according to the condenser inlet temperature and the condenser outlet temperature; if the temperature difference between the condenser inlet and outlet is less than the first preset temperature difference, generating a refrigerant shortage signal and / or a fouling signal; and if the temperature difference between the condenser inlet and outlet is greater than the second preset temperature difference, generating a refrigerant temperature too low signal and / or a refrigerant flow rate too large signal.

[0011] In combination with the first aspect, in a possible implementation manner, the obtaining of the evaporation and condensation conditions of the evaporator and the condenser includes: obtaining the condenser inlet pressure and the condenser outlet pressure; the generating of the corresponding evaporation and condensation prompt signal according to the evaporation and condensation conditions and the third standard condition includes: if the condenser inlet pressure is greater than the first preset pressure, generating a refrigerant excess signal and / or a condenser blockage signal; and if the condenser outlet pressure is less than the second preset pressure, generating a refrigerant leakage signal and / or an expansion valve opening abnormal signal.

[0012] In combination with the first aspect, in a possible implementation method, obtaining the evaporation and condensation operating conditions of the evaporator and condenser includes: adjusting the air-conditioning unit to a set standard operating condition and running it for more than a preset standard time; controlling the thermal imager to be perpendicular to the condenser surface at a preset distance to collect temperature data of the heat exchanger tube bundle area; and dividing the heat exchanger tube bundle area into multiple cell areas; generating corresponding evaporation and condensation prompt signals according to the evaporation and condensation operating conditions and the third standard operating conditions includes: if the temperature change of adjacent cell areas is greater than a preset temperature difference gradient, a refrigerant cross-flow signal is generated; a first number of the cell areas are formed into a test area, and if the temperature difference of the test area is greater than a preset local temperature difference, a refrigerant cross-flow signal is generated; and if the standard deviation of the temperature data of the entire heat exchanger tube bundle area is greater than the third preset temperature difference, a refrigerant cross-flow signal is generated.

[0013] In combination with the first aspect, in a possible implementation method, the comparing the occurrence moments of the compression abnormality signal, the mechanical fault signal and the evaporation and condensation prompt signal, and calculating the occurrence correlation quantity between the signals include: within the historical preset time length, if two or more signals within the preset unit time length occur simultaneously, the corresponding signals are counted as one occurrence correlation quantity; the generating of signal risk assessment data based on the occurrence correlation quantity includes: generating a corresponding fault risk level based on the corresponding occurrence correlation quantities of the compression abnormality signal, the mechanical fault signal and the evaporation and condensation prompt signal.

[0014] Based on the above technical solution, the present invention has the following beneficial effects: The present invention performs fault detection on compression conditions, compressor vibration, and evaporation and condensation data, comprehensively covering electrical, mechanical, and thermal system faults, and conducting multi-dimensional diagnostic assessments. It also correlates historical data to avoid single-point false alarms, analyzes fault development trends, and can identify intermittent faults or cumulative damage. The present invention uses temporal correlation of statistical signals to eliminate isolated abnormal faults through correlation analysis, thereby focusing on the real fault chain, quantifying the causal probability between faults through temporal correlation, and improving diagnostic reliability. The present invention eliminates false alarms through time correlation and improves diagnostic reliability. Moreover, after calculating the correlation quantity, it can quantitatively analyze the correlation between various faults, perform different levels of fault diagnosis evaluation for different correlation quantities, and focus on monitoring signals with high correlation quantities, providing higher priority and more computing power for signal monitoring with high correlation quantities, thereby significantly improving the fault detection rate and diagnostic efficiency of precision air-conditioning units. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 FIG2 is a schematic diagram of the method steps of a water-cooled precision air-conditioning unit fault diagnosis method provided by an embodiment of the present application; Figure 2 The figure shows a schematic diagram of the steps of a method for detecting abnormal compression ratio signals provided by an embodiment of the present application; Figure 3 The figure shows a schematic diagram of the steps of a method for detecting liquid hammer risk signals provided by an embodiment of the present application; Figure 4 The figure shows a schematic diagram of the steps of a method for detecting abnormal power compressor speed signals provided by an embodiment of the present application; Figure 5 The figure shows a schematic diagram of the steps of a method for detecting inner bearing race fault signals and roller fault signals provided by an embodiment of the present application; Figure 6 The figure shows a schematic diagram of the steps of a method for detecting refrigerant shortage signals, refrigerant excess signals, and normal charging signals provided by an embodiment of the present application; Figure 7 The figure shows a schematic diagram of the steps of a method for detecting refrigerant shortage signals and / or fouling signals, refrigerant temperature too low signals and / or refrigerant flow rate too large signals provided by an embodiment of the present application; Figure 8 The figure shows a schematic diagram of the steps of a method for detecting refrigerant excess signals and / or condenser blockage signals, refrigerant leakage signals and / or abnormal expansion valve opening signals provided by an embodiment of the present application; Figure 9 The figure shows a schematic diagram of the steps of a method for detecting refrigerant cross - gas signals provided by an embodiment of the present application; Figure 10 The figure shows a schematic diagram of the steps of a method for calculating correlation quantities and matching fault risk levels provided by an embodiment of the present application. Detailed implementation manners

[0016] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0017] Figure 1 The figure shows a schematic diagram of the steps of a fault diagnosis method for a water - cooled precision air - conditioning unit provided by an embodiment of the present application. The present application provides a fault diagnosis method for a water - cooled precision air - conditioning unit. In one embodiment, as Figure 1 shown, the fault diagnosis method for the water - cooled precision air - conditioning unit includes: Step 110, obtain the compression working condition of the compressor.

[0018] In this step, key operating parameters of the compressor are collected in real time through sensors, such as suction / discharge pressure data, temperature data, power compressor speed data, etc., providing a data basis for subsequent fault diagnosis. This step can ensure that the data covers the core working state of the compressor, reflecting its actual operating efficiency and health.

[0019] Step 120: Compare the compression condition with the first standard condition to generate a compression anomaly signal.

[0020] In this step, comparing the collected data with the preset standard condition can identify anomalies deviating from the normal value. Generating an anomaly signal can trigger early warning to avoid cascading failures caused by abnormal compressor conditions.

[0021] Step 130: Obtain the vibration data of the compressor.

[0022] In this step, collecting the mechanical vibration spectrum through a vibration sensor (such as an accelerometer) can reflect some mechanical faults, such as bearing wear and rotor imbalance. Vibration analysis can thus detect hidden mechanical damage and improve the comprehensiveness of fault diagnosis.

[0023] Step 140: Generate corresponding mechanical fault signals based on the vibration data and the second standard condition.

[0024] In this step, comparing the vibration characteristics (such as amplitude and frequency components) with the standard healthy vibration model can locate specific fault types. Different from the electrical anomalies analyzed in Step 120, this step can detect mechanical faults and improve the diagnostic accuracy.

[0025] Step 150: Obtain the evaporation and condensation conditions of the evaporator and condenser.

[0026] In this step, collecting key parameters of the evaporator and condenser, such as the temperature of specific components of the evaporator, the working condition of the evaporator pipeline, the temperature of specific components of the condenser, the working condition of the condenser pipeline, etc., can be used to evaluate the refrigerant state, etc.

[0027] Step 160: Generate corresponding evaporation and condensation prompt signals based on the evaporation and condensation conditions and the third standard condition.

[0028] In this step, generating a prompt signal based on the evaporation and condensation conditions can assist in judging whether the root cause of the fault is due to refrigerant factors rather than the compressor itself.

[0029] Step 170: Retrospect the historical preset duration from the current moment and retrieve all compression anomaly signals, mechanical fault signals, and evaporation and condensation prompt signals.

[0030] In this step, historical data is correlated (for example, historical records with a preset time period of 3, 6, 12, or 24 hours) to avoid single-point false alarms and analyze fault development trends. Time series analysis can identify intermittent faults or cumulative damage (such as slowly increasing vibration).

[0031] Step 180 : Compare the occurrence times of the compression abnormality signal, the mechanical failure signal, and the evaporation and condensation prompt signal, and calculate the occurrence correlation between the signals.

[0032] In this step, the temporal correlation of the signals is statistically analyzed, such as whether the vibration fault is accompanied by evaporation and condensation abnormalities, and the causal probability between faults is quantified. Through correlation analysis, isolated abnormal faults can be eliminated, thereby focusing on the real high-frequency fault chain.

[0033] Step 190: Generate signal risk assessment data based on the occurrence correlation quantity.

[0034] In this step, the risk assessment of the integrated multi-signal correlation output can be used to perform hierarchical diagnostic fault assessment based on the correlation frequency of each signal, and can give priority to predictive maintenance monitoring and focused maintenance monitoring for faults with high correlation.

[0035] When this embodiment is applied, it can perform multi-dimensional diagnosis, combining compression conditions, compressor vibration, evaporation and condensation data, covering electrical, mechanical, and thermal system faults. The causal probability between faults is quantified through time correlation, thereby improving diagnostic reliability. After calculating the correlation quantity, different levels of fault diagnosis evaluation can be performed on different correlation quantities, and the focus can be on monitoring signals with high correlation quantities, which can provide higher priority and more computing power for signal monitoring with high correlation quantities. This method significantly improves the fault detection rate and diagnostic efficiency of precision air-conditioning units, performs correlation evaluation on each fault, and can perform multi-dimensional diagnostic evaluation on the fault.

[0036] Figure 2 FIG. 1 is a schematic diagram of a method for detecting an abnormal compression ratio signal according to an embodiment of the present application. Figure 2 As shown, step 110 includes: Step 1101: Obtain the compressor exhaust port pressure and the compressor intake port pressure.

[0037] In this step, pressure sensors collect real-time pressure at the exhaust and intake ports, reflecting the compressor's actual workload and the refrigerant cycle status. Compared to inferring pressure from current or temperature, direct measurement avoids the cumulative errors caused by indirect data conversion, resulting in more reliable data.

[0038] Step 1102: derive a compression ratio based on the compressor exhaust pressure and the compressor intake pressure.

[0039] In this step, the compression ratio is calculated by dividing the compressor exhaust pressure by the compressor suction pressure. The compression ratio can reflect the matching degree between the compressor's work capacity and the system resistance.

[0040] Step 120 includes: Step 1201: Obtain the design compression ratio of the compressor of the air-conditioning unit.

[0041] In this step, the design compression ratio is retrieved from the compressor design parameters, such as the standard value given by the manufacturer, as an objective benchmark for fault judgment, avoiding the subjectivity of the empirical threshold.

[0042] Step 1202: If the difference between the detected compression ratio and the designed compression ratio is greater than the preset compression difference, a compression ratio abnormality signal is generated.

[0043] In this step, the allowable fluctuation range is defined by presetting the compression difference (e.g., ±10%) to avoid false alarms caused by fluctuations in normal operating conditions (e.g., start-stop transients). This allows the detection of significantly abnormal compression ratios to be captured and generates a compression ratio abnormality signal, which can be used for alarm, recording, and analysis.

[0044] This embodiment can monitor the compression ratio and analyze the compression ratio abnormality signal in the compression abnormality signal. The compression ratio abnormality is directly related to the core failure of the compressor (such as valve leakage, system blockage). Through this embodiment, the compression ratio abnormality signal can be analyzed for correlation with the mechanical failure signal and the evaporation and condensation prompt signal.

[0045] Figure 3 FIG. 1 is a schematic diagram of a method for detecting a liquid hammer risk signal according to an embodiment of the present application. Figure 3 As shown, step 110 includes: Step 1103: Obtain the compressor suction port pressure and the compressor suction port temperature.

[0046] In this step, the state of the air intake is collected in real time through the pressure sensor and temperature sensor to provide a data basis for liquid hammer judgment.

[0047] Step 1104: derive the corresponding saturation temperature according to the compressor suction port pressure.

[0048] In this step, the saturation temperature corresponding to different inlet pressures is pre-set. This can be the boiling point of the refrigerant at that inlet pressure. The saturation temperature is a critical threshold for determining whether the refrigerant is fully vaporized. If the inlet temperature is lower than this value, there may be unevaporated liquid refrigerant.

[0049] Step 120 includes: Step 1203: Obtain a reference temperature according to the saturation temperature and the correction temperature.

[0050] In this step, the saturation temperature is offset and adjusted (e.g., +2°C) based on system characteristics (e.g., pipeline heat loss, sensor error) to avoid misjudgment due to differences between theoretical values and actual operating conditions.

[0051] Step 1204: If the compressor suction port temperature is lower than the reference temperature, a liquid hammer risk signal is generated.

[0052] In this step, if the suction port temperature falls below the reference temperature, the refrigerant is not fully vaporized, and liquid refrigerant may enter the compressor, posing a risk of liquid hammer. Once a liquid hammer risk signal is generated, an audible and visual alarm or control system linkage prompts the operator to adjust the expansion valve opening or check the evaporator liquid supply. Alternatively, the compressor load can be reduced or intermittent operation can be initiated to prevent liquid refrigerant from impacting the cylinder, leading to mechanical failures such as valve plate breakage and bearing damage.

[0053] When this embodiment is applied, the saturation temperature comparison based on thermodynamic principles is more reliable than relying solely on a single parameter of temperature or pressure. By correcting the temperature to adapt to different system configurations (such as long pipelines and high superheat requirements), the limitations of the reference temperature are avoided. This embodiment can issue an early warning before the liquid refrigerant actually enters the compressor to avoid irreversible mechanical damage. For example, when the refrigerant supply on the evaporator side is too much (such as an expansion valve failure), it may cause the suction port temperature to drop rapidly. A liquid shock risk signal can be generated before the liquid shock occurs, thus buying critical time for system adjustment. This embodiment improves the defense capability of precision air-conditioning units against liquid shock, a malicious fault, and is particularly suitable for computer room air-conditioning systems with high reliability requirements. Through this embodiment, the liquid shock risk signal can be analyzed for correlation with the mechanical failure signal and the evaporation and condensation prompt signal.

[0054] Figure 4 FIG. 1 is a schematic diagram of a method for detecting abnormal power and speed signals of a compressor according to an embodiment of the present application. Figure 4 As shown, step 110 includes: Step 1105: Acquire the actual power of the compressor and monitor the compressor speed in real time.

[0055] In this step, the speed of the compressor when the compressor is operating power is directly measured by a current, voltage and speed sensor or a power meter to reflect its actual load state.

[0056] Step 120 includes: Step 1205: Compare the compressor speed with the rated speed. If the fluctuation of the compressor speed relative to the rated speed exceeds a preset fluctuation threshold, generate a speed abnormality signal. If the actual power exceeds the preset amplitude of the rated power, generate a power abnormality signal.

[0057] In this step, when the actual compressor speed power exceeds a preset amplitude (which can be set to ±15%) of the rated power speed to a certain extent, an alarm is triggered to prevent the compressor from burning out or having its lifespan shortened due to long-term abnormal speed and overloading operation. Abnormal speed may be caused by factors such as control system failures, sensor abnormalities, mechanical component problems, electrical faults, or external environmental impacts. Excessive power may be caused by poor condenser heat dissipation, excessive refrigerant, or increased mechanical friction (such as poor lubrication). Low power may indicate refrigerant leakage, insufficient suction pressure, or internal wear of the compressor (such as valve leakage). Frequency reduction and speed reduction or shutdown can be executed to avoid serious accidents caused by long-term abnormal power. Power abnormalities may be caused by mechanical failures (such as bearing jamming), electrical problems (such as motor winding short circuits), or system-side abnormalities (such as too high condensation pressure). Through this embodiment, the power abnormality signal can be analyzed for mutual correlation with the mechanical failure signal and the evaporation and condensation prompt signal. Specifically, if the compressor speed is lower than 95% of the rated speed and lasts for more than 10 seconds, a speed deficiency signal is generated; if the compressor speed is higher than 105% of the rated speed and lasts for more than 10 seconds, a speed overload signal is generated; if the fluctuation range of the compressor speed within 1 minute is greater than ±8%, a speed instability signal is generated.

[0058] Figure 5 The following is a schematic diagram of the method steps for detecting the inner ring fault signal and the roller fault signal provided by an embodiment of the present application. In one embodiment, as Figure 5 shown, step 140 includes: Step 1401: Convert the vibration data of the time-domain signal into frequency-domain data.

[0059] In this step, the time-domain vibration waveform is converted into a frequency-domain spectrum through Fourier transform (FFT) to highlight the frequency components of the vibration signal, facilitating the identification of periodic fault characteristics. Frequency-domain analysis can filter random noise and focus on the characteristic frequencies related to mechanical faults (such as bearing defect frequencies, roller imbalance frequencies).

[0060] Step 1402: Extract the first spectrum at 1.5 times the rotational frequency and the second spectrum at 3 times the rotational frequency according to the frequency-domain data.

[0061] In this step, the fault characteristics are located: 1.5 times the rotational frequency is the characteristic frequency component of the inner ring fault of the bearing (such as cracks, spalling); 3 times the rotational frequency is the characteristic frequency component of the rolling element roller fault (such as wear, fragmentation). By focusing on specific multiple spectra, the complexity of full-spectrum analysis is avoided, and the diagnostic efficiency is improved.

[0062] Step 1403: If the peak value of the first spectrum is greater than the first preset peak value, an inner ring fault signal of the bearing is generated.

[0063] In this step, when the peak value of the first spectrum corresponding to 1.5 times the rotational frequency exceeds the threshold, for example, exceeds three times the historical baseline value, it is determined that there is local damage to the inner ring of the bearing, and a fault signal is triggered.

[0064] Step 1404: If the peak value of the second spectrum is greater than the second preset peak value, a roller fault signal is generated.

[0065] In this step, when the peak value of the second spectrum corresponding to 3 times the rotational frequency exceeds the threshold, for example, exceeds three times the historical baseline value, it is determined that the roller is worn or missing. Timely maintenance can prevent the bearing from jamming.

[0066] Through this embodiment, the inner ring fault signal, roller fault signal of the bearing can be analyzed for mutual correlation with the compression anomaly signal and evaporation condensation prompt signal.

[0067] Figure 6 The figure shows a schematic diagram of the method steps for detecting a refrigerant shortage signal, refrigerant excess signal, and normal charging signal provided by an embodiment of the present application. In one embodiment, as Figure 6 shown, step 150 includes: Step 1501: Obtain the evaporator outlet temperature, evaporator pipeline pressure, condenser outlet temperature, and condenser pipeline pressure.

[0068] In this step, the outlet states and pipeline pressures of the evaporator and condenser are monitored in real time through temperature and pressure sensors, comprehensively reflecting the phase change process of the refrigerant in the heat exchanger. The evaporator outlet temperature and evaporator pipeline pressure are related to the refrigerant heat absorption effect, and the condenser outlet temperature and condenser pipeline pressure are related to the heat dissipation effect, providing a data basis for subsequent superheat / subcooling calculation.

[0069] Step 160 includes: Step 1601: Retrieve the evaporator saturation temperature corresponding to the evaporator pipeline pressure, and retrieve the condenser saturation temperature corresponding to the condenser pipeline pressure.

[0070] In this step, based on the refrigerant physical property parameters (such as the pressure-saturation temperature conversion table or equation), the measured pressure is converted into the theoretical saturation temperature as the benchmark for judging whether the refrigerant phase change is complete. The saturation temperature changes in real time with the pressure, avoiding misjudgment caused by a fixed threshold (such as the normal increase in the condensation pressure of the condenser pipeline pressure under high load).

[0071] Step 1602: Obtain the evaporator superheat according to the evaporator outlet temperature and the evaporator saturation temperature.

[0072] In this step, the superheat = evaporator outlet temperature - evaporator saturation temperature, reflecting whether the refrigerant in the evaporator is completely evaporated.

[0073] Step 1603: Obtain condenser subcooling according to the condenser outlet temperature and the condenser saturation temperature.

[0074] In this step, subcooling = condenser saturation temperature - condenser outlet temperature, which reflects whether the refrigerant in the condenser is fully condensed.

[0075] Step 1604: If the evaporator superheat is greater than the first superheat and the evaporator pipeline pressure is lower than the first pressure standard value, a refrigerant shortage signal is generated.

[0076] In this step, if the evaporator superheat exceeds the specified value (e.g., >10°C) and the evaporator line pressure falls below a first pressure threshold (e.g., 0.6 MPa), this indicates that the refrigerant in the evaporator has completely evaporated prematurely, possibly due to leakage or insufficient recharge. Once a refrigerant shortage signal is triggered, a refrigerant shortage signal and / or scaling signal can be generated to initiate refrigerant replenishment or restrict compressor operation to prevent compressor overheating due to refrigerant shortage.

[0077] Step 1605: If the condenser subcooling degree is less than the second superheating degree and the condenser line pressure is higher than the second pressure standard value, a refrigerant excess signal is generated.

[0078] In this step, if the condenser subcooling temperature is too low (e.g., <3°C) and the condenser line pressure is greater than a second pressure threshold (e.g., 0.8 MPa), this indicates insufficient liquid refrigerant in the condenser, possibly due to overcharging or decreased condensing efficiency. Triggering the refrigerant excess signal prevents excess refrigerant from flowing back into the compressor, causing liquid hammer or excessive condensing pressure in the condenser line.

[0079] Step 1606: If the evaporator superheat, the evaporator line pressure, the condenser subcooling, and the condenser line pressure are all within the threshold fluctuation range, if the evaporator superheat is less than or equal to the first superheat, and the condenser subcooling is greater than or equal to the second superheat, then a normal charging signal is generated.

[0080] In this step, if the evaporator superheat, evaporator line pressure, condenser subcooling, and condenser line pressure are all within reasonable ranges, the refrigerant charge and system operation are considered normal and no intervention is required. This provides a quantitative basis for periodic maintenance, eliminating the need to blindly adjust the refrigerant inventory.

[0081] Through this embodiment, the correlation analysis of the compression abnormality signal, mechanical failure signal, refrigerant shortage signal, and refrigerant excess signal can be performed. If two or more signals occur simultaneously within a preset unit time, the corresponding signals are counted as one occurrence of correlation.

[0082] Figure 7The figure shows a schematic diagram of the method steps for detecting a refrigerant shortage signal and / or a fouling signal, a refrigerant temperature too low signal and / or a refrigerant flow rate too high signal provided by an embodiment of the present application. In one embodiment, as Figure 7 shown, step 150 includes: Step 1502, obtain the condenser inlet temperature and the condenser outlet temperature.

[0083] In this step, the condenser inlet temperature and the condenser outlet temperature are collected in real time through a temperature sensor to reflect the heat exchange efficiency of the condensation process. The condenser inlet temperature reflects the exhaust state of the compressor, and the condenser outlet temperature reflects the condensation effect, providing basic data for the temperature difference analysis.

[0084] Step 160 includes: Step 1607, calculate the temperature difference between the condenser inlet and outlet according to the condenser inlet temperature and the condenser outlet temperature.

[0085] In this step, a temperature difference quantification analysis is performed to calculate the temperature difference between the condenser inlet and outlet, that is, the condenser inlet temperature minus the condenser outlet temperature, which directly characterizes the heat dissipation ability of the refrigerant in the condenser. A reasonable temperature difference (such as 5 - 15 °C) indicates that the heat exchange efficiency of the condenser is normal. When the temperature difference deviates from the reasonable range, it indicates that there is a fault in the system or the operating condition is abnormal.

[0086] Step 1608, if the temperature difference between the condenser inlet and outlet is less than the first preset temperature difference, generate a refrigerant shortage signal and / or a fouling signal.

[0087] In this step, if the temperature difference between the condenser inlet and outlet is too small (such as < 3 °C), it may be due to insufficient refrigerant flow rate, resulting in premature cooling of the refrigerant in the condenser. The reasons may be refrigerant shortage or refrigerant fouling, which in turn leads to insufficient heat dissipation. Subsequently, the condenser can be cleaned or the refrigerant charge can be checked according to the refrigerant shortage signal and / or the fouling signal.

[0088] Step 1609, if the temperature difference between the condenser inlet and outlet is greater than the second preset temperature difference, generate a refrigerant temperature too low signal and / or a refrigerant flow rate too high signal.

[0089] In this step, if the temperature difference between the condenser inlet and outlet is too large (such as the second preset temperature difference is set to 20 °C), it may be due to too low cooling water / air temperature (such as low ambient temperature in winter). The reasons may be a refrigerant temperature too low signal and / or a refrigerant flow rate too high signal, which in turn leads to excessive condensation. Subsequently, the refrigerant flow rate can be controlled or the expansion valve opening can be adjusted according to the refrigerant temperature too low signal and / or the refrigerant flow rate too high signal to optimize the system operating state.

[0090] Through this embodiment, the compression anomaly signal, mechanical fault signal, refrigerant shortage signal and / or fouling signal, and refrigerant temperature too low signal and / or refrigerant flow rate too high signal can be analyzed for mutual correlation.

[0091] Figure 8 The following is a schematic diagram of the method steps for detecting a refrigerant overcharge signal and / or a condenser blockage signal, a refrigerant leakage signal and / or an expansion valve opening anomaly signal provided by an embodiment of the present application. In one embodiment, as Figure 8 shown, step 150 includes: Step 1503: Obtain the condenser inlet pressure and the condenser outlet pressure.

[0092] In this step, the refrigerant pressures at the inlet and outlet of the condenser are collected in real time through a pressure sensor to reflect the state of the high-pressure side system. The inlet pressure is related to the exhaust performance of the compressor, and the outlet pressure is related to the condensation effect and the subsequent throttling process, providing data support for pressure anomaly diagnosis.

[0093] Step 160 includes: Step 1610: If the condenser inlet pressure is greater than a first preset pressure, generate a refrigerant overcharge signal and / or a condenser blockage signal.

[0094] In this step, if the inlet pressure is too high, for example, the first preset pressure is 15% exceeding the system-set rated value, it may be due to excessive accumulation of liquid refrigerant in the condenser or condenser blockage, resulting in a decrease in heat dissipation efficiency. After triggering the refrigerant overcharge and / or condenser blockage signal, refrigerant recovery or condenser cleaning can be performed to avoid compressor high-pressure protection shutdown.

[0095] Step 1611: If the condenser outlet pressure is less than a second preset pressure, generate a refrigerant leakage signal and / or an expansion valve opening anomaly signal.

[0096] In this step, if the condenser outlet pressure is too low, for example, the second preset pressure is 20% lower than the system-set rated value, it may be due to refrigerant leakage and / or an abnormal expansion valve opening, resulting in insufficient system circulation or an overly large expansion valve opening or control failure. After triggering the refrigerant leakage signal and / or expansion valve opening anomaly signal, a leak detection alarm or an expansion valve calibration prompt can be generated to prevent the continuous deterioration of system efficiency.

[0097] Through this embodiment, the compression anomaly signal, mechanical fault signal, refrigerant overcharge signal and / or condenser blockage signal can be analyzed for mutual correlation.

[0098] Figure 9 The following is a schematic diagram of the method steps for detecting a refrigerant cross-leakage signal provided by an embodiment of the present application. In one embodiment, as Figure 9 shown, step 150 includes: Step 1504: Adjust the air-conditioning unit to a set standard operating condition and operate it for a period greater than a preset standard time.

[0099] In this step, the air conditioner is adjusted to a set standard operating condition, with uniform operating parameters such as rated load and fixed ambient temperature, to eliminate variable interference and ensure comparable test results. The system is allowed to operate for a sufficient time (e.g., 30 minutes) to achieve thermal equilibrium and avoid transient temperature fluctuations that could affect diagnostic accuracy.

[0100] Step 1505 : Control the thermal imager to be perpendicular to the condenser surface at a preset distance to collect temperature data of the heat exchanger tube bundle area.

[0101] In this step, an infrared thermal imager captures the entire condenser surface temperature distribution, avoiding the blind spots associated with traditional point-based temperature measurement. A fixed distance (e.g., 1 meter) and vertical angle ensure consistent image scale, effectively improving the spatial resolution of the temperature data.

[0102] Step 1506: Divide the heat exchanger tube bundle area into multiple unit cell areas.

[0103] In this step, the thermal image is divided into pixel grid units. Multiple pixel grid units are then grouped into a cell area. For example, a cell area of 5×5 pixels, 8×8 pixels, or 10×10 pixels can be grouped into a cell area to locate local temperature anomalies. The cell size can be dynamically adjusted to match the tube bundle density of different condensers.

[0104] Step 160 includes: Step 1612: If the temperature change of adjacent cell areas is greater than the preset temperature difference gradient, a refrigerant cross-flow signal is generated.

[0105] In this step, a sudden increase in the temperature difference between adjacent cell areas (for example, the preset temperature gradient is set to 5°C) indicates uneven refrigerant flow, which may be caused by partial pipe blockage or gas-liquid mixing leading to gas cross-talk.

[0106] Step 1613: Group the first number of cell areas into a test area, and if the temperature difference in the test area is greater than the preset local temperature difference, generate a refrigerant cross-flow signal.

[0107] In this step, a test area is formed by combining multiple cell areas. For example, a 3×3 adjacent cell area is combined into a test area. This highlights regional temperature imbalances and avoids misjudgments caused by a single cell area, improving robustness to real cross-flow faults. The temperature difference within the test area is the highest temperature minus the lowest temperature within the test area. The preset local temperature difference can be set to 5°C. A temperature difference greater than the preset local temperature difference indicates regional temperature imbalance, which may be caused by partial pipe blockage or gas-liquid mixing.

[0108] Step 1614: If the standard deviation of the temperature data in the entire heat exchanger tube bundle area is greater than the third preset temperature difference, a refrigerant cross - leakage signal is generated.

[0109] In this step, macroscopic uniformity assessment is carried out, the standard deviation of the temperature in the whole area is calculated to quantify the overall heat dissipation uniformity. If the standard deviation exceeds the standard (for example, the third preset temperature difference is set to 8 °C), it can be determined that there is an abnormal refrigerant distribution at the system level, such as multi - pipeline cross - leakage or distributor failure.

[0110] Through this embodiment, the compression abnormal signal, mechanical failure signal and refrigerant cross - leakage signal can be analyzed for mutual correlation.

[0111] Figure 10 The figure shows a schematic diagram of the method steps for calculating the correlation quantity and matching the failure risk level provided by an embodiment of the present application. In one embodiment, as Figure 10 shown, step 180 includes: Step 1801: Within the historical preset duration, if two or more signals occur simultaneously within the preset unit duration, the corresponding signals are counted as one occurrence of the correlation quantity.

[0112] In this step, by counting the co - occurrence times of signals within a fixed time window (i.e., the preset unit duration, for example, set to 5 minutes), the correlation between faults is quantified to avoid misjudgment of isolated signals. Multi - fault coupling recognition can be achieved. For example, when the compression abnormal signal and the evaporation - condensation prompt signal frequently occur simultaneously, it can be determined that there is a system - level refrigerant circulation fault, rather than a single - component problem. Specifically, when two or more signals occur simultaneously within the preset unit duration, all the simultaneously occurring signals are counted as one correlation quantity. For example, within the historical 3 hours, if the compression abnormal signal, mechanical failure signal, and evaporation - condensation prompt signal occur simultaneously, each of these three signals is counted as one correlation quantity; if the compression abnormal signal and mechanical failure signal occur simultaneously, each of these two signals is counted as one correlation quantity. The more the correlation quantity of a certain signal, the more faults that signal may induce.

[0113] Step 190 includes: Step 1901: Generate corresponding failure risk levels according to the occurrence correlation quantities corresponding to the compression abnormal signal, mechanical failure signal, and evaporation - condensation prompt signal.

[0114] In this step, the compression abnormal signals can be further divided into compression ratio abnormal signals, liquid hammer risk signals, power and speed abnormal signals, and the mechanical fault signals can be further divided into inner race fault signals of bearings and roller fault signals. The evaporation and condensation prompt signals can be further divided into refrigerant shortage signals, refrigerant excess signals, normal charging signals, refrigerant shortage signals, fouling signals, refrigerant temperature too low signals, refrigerant flow rate too large signals, refrigerant excess signals, condenser blockage signals, refrigerant leakage signals, expansion valve opening abnormal signals, refrigerant cross - gas signals. The corresponding occurrence correlation quantities in step 180 can be subjected to correlation analysis among all the above 17 sub - signals, or among the compression abnormal signals, mechanical fault signals and evaporation and condensation prompt signals, or among the 3 sub - signals under the compression abnormal signals, mechanical fault signals and evaporation and condensation prompt signals, or among the compression abnormal signals, evaporation and condensation prompt signals and 2 sub - signals under the mechanical fault signals, or among the 12 sub - signals under the compression abnormal signals, mechanical fault signals and evaporation and condensation prompt signals, or among the 3 sub - signals under the compression abnormal signals, 2 sub - signals under the mechanical fault signals and evaporation and condensation prompt signals, or among the 3 sub - signals under the compression abnormal signals, mechanical fault signals and 12 sub - signals under the evaporation and condensation prompt signals, or among the 2 sub - signals under the compression abnormal signals, mechanical fault signals and 12 sub - signals under the evaporation and condensation prompt signals.

[0115] In this step, the risk dynamics are classified as follows: for example, if the number of correlation quantities is > 1 and ≤ 3, it can be determined that the signal is of low risk, and it is recommended to mainly observe; for example, if the number of correlation quantities is > 3 and ≤ 6, it can be determined that the signal is of medium risk, with a potential fault trend, and an early warning can be triggered and recorded; for example, if the number of correlation quantities is > 6 and ≤ 10, it can be determined as medium - high risk, indicating that multiple signals are strongly correlated at this time and the potential fault trend is higher; for example, if the number of correlation quantities is 10 and ≤ 15, it can be determined as high risk, indicating that multiple signals are strongly correlated at this time and the system can be shut down for maintenance immediately. The quantities 3, 6, 10, 15 above are for illustrative purposes only. In actual applications, classification settings are made according to the maximum and minimum number of correlation quantities or according to expert experience. For example, within a historical preset time period, the maximum number of correlation quantities is 20 times and the minimum number of correlation quantities is 5 times, then it can be classified as: 5 - 8 is low risk, 8 - 12 is medium risk, 12 - 16 is medium - high risk, 16 - 20 is high risk. This step filters out random interferences through a time window to highlight the real fault chain. High - correlation - quantity signals can more accurately point to the system - level fault source, and it shows that high - correlation - quantity signals may cause more faults and need to be given higher attention, that is, a higher fault risk level.

[0116] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0117] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0118] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0119] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be applied in the widest sense consistent with the principles and novel features of the present invention.

[0120] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A fault diagnosis method for a water-cooled precision air conditioner unit, characterized in that, Including: Obtain the compression operating condition of the compressor; Compare the compression operating condition with the first standard operating condition to generate a compression anomaly signal; Obtain the vibration data of the compressor; Generate a corresponding mechanical fault signal according to the vibration data and the second standard operating condition; Obtain the evaporation and condensation operating conditions of the evaporator and the condenser; Generate a corresponding evaporation and condensation prompt signal according to the evaporation and condensation operating condition and the third standard operating condition; Retrospect the historical preset duration according to the current moment, and retrieve all the compression anomaly signals, the mechanical fault signals, and the evaporation and condensation prompt signals; Compare the occurrence moments of the compression anomaly signals, the mechanical fault signals, and the evaporation and condensation prompt signals, and calculate the occurrence correlation quantity between the signals; And Generate signal risk assessment data according to the occurrence correlation quantity.

2. The fault diagnosis method for a water-cooled precision air conditioner unit according to claim 1, wherein The obtaining the compression operating condition of the compressor includes: Obtain the compressor discharge port pressure and the compressor suction port pressure; and Obtain the detected compression ratio according to the compressor discharge port pressure and the compressor suction port pressure; The comparing the compression operating condition with the first standard operating condition to generate a compression anomaly signal includes: Obtain the designed compression ratio of the compressor of the air conditioner unit; and If the difference between the detected compression ratio and the designed compression ratio is greater than the preset compression difference, generate a compression ratio anomaly signal.

3. The fault diagnosis method for a water-cooled precision air conditioner unit according to claim 1, wherein The obtaining the compression operating condition of the compressor includes: Obtain the compressor suction port pressure and the compressor suction port temperature; Retrieve the corresponding saturation temperature according to the compressor suction port pressure; The comparing the compression operating condition with the first standard operating condition to generate a compression anomaly signal includes: Obtain the reference temperature according to the saturation temperature and the corrected temperature; If the compressor suction port temperature is less than the reference temperature, generate a liquid slugging risk signal.

4. The fault diagnosis method for a water-cooled precision air conditioner unit according to claim 1, wherein The obtaining the compression operating condition of the compressor includes: Obtain the actual power of the compressor and monitor the compressor speed in real time; The comparing the compression operating condition with the first standard operating condition to generate a compression anomaly signal includes: If the actual power exceeds the preset amplitude of the rated power, generate a power anomaly signal. Compare the compressor speed with the rated speed. If the fluctuation of the compressor speed relative to the rated speed exceeds the preset fluctuation threshold, generate a speed anomaly signal.

5. The fault diagnosis method for a water-cooled precision air conditioner unit according to claim 1, wherein The generating a corresponding mechanical fault signal according to the vibration data and the second standard operating condition includes: Convert the vibration data of the time domain signal into frequency domain data; Extract the first frequency spectrum at 1.5 times the rotational frequency and the second frequency spectrum at 3 times the rotational frequency according to the frequency domain data; If the peak value of the first frequency spectrum is greater than the first preset peak value, generate a bearing inner race fault signal; and If the peak value of the second frequency spectrum is greater than the second preset peak value, generate a roller fault signal.

6. The fault diagnosis method for a water-cooled precision air conditioner unit according to claim 1, wherein The obtaining of the evaporation and condensation conditions of the evaporator and the condenser includes: Obtaining the evaporator outlet temperature, the evaporator pipeline pressure, the condenser outlet temperature, and the condenser pipeline pressure; The generating of the corresponding evaporation and condensation prompt signal according to the evaporation and condensation conditions and the third standard condition includes: Retrieving the evaporator saturation temperature corresponding to the evaporator pipeline pressure, and retrieving the condenser saturation temperature corresponding to the condenser pipeline pressure; Obtaining the evaporator superheat degree based on the evaporator outlet temperature and the evaporator saturation temperature; Obtaining the condenser subcooling degree based on the condenser outlet temperature and the condenser saturation temperature; If the evaporator superheat degree is greater than the first superheat degree and the evaporator pipeline pressure is lower than the first pressure standard value, generating a refrigerant shortage signal; If the condenser subcooling degree is less than the second superheat degree and the condenser pipeline pressure is higher than the second pressure standard value, generating a refrigerant excess signal; and If the evaporator superheat degree, the evaporator pipeline pressure, the condenser subcooling degree, and the condenser pipeline pressure are all within the threshold fluctuation range, and if the evaporator superheat degree is less than or equal to the first superheat degree and the condenser subcooling degree is greater than or equal to the second superheat degree, generating a normal charging signal.

7. The fault diagnosis method for the water-cooled precision air-conditioning unit according to claim 1, characterized in that The obtaining of the evaporation and condensation conditions of the evaporator and the condenser includes: Obtaining the condenser inlet temperature and the condenser outlet temperature; The generating of the corresponding evaporation and condensation prompt signal according to the evaporation and condensation conditions and the third standard condition includes: Calculating the temperature difference between the condenser inlet and outlet based on the condenser inlet temperature and the condenser outlet temperature; If the temperature difference between the condenser inlet and outlet is less than the first preset temperature difference, generating a refrigerant shortage signal and / or a fouling signal; and If the temperature difference between the condenser inlet and outlet is greater than the second preset temperature difference, generating a refrigerant temperature too low signal and / or a refrigerant flow rate too large signal.

8. The fault diagnosis method for the water-cooled precision air-conditioning unit according to claim 1, characterized in that The obtaining of the evaporation and condensation conditions of the evaporator and the condenser includes: Obtaining the condenser inlet pressure and the condenser outlet pressure; The generating of the corresponding evaporation and condensation prompt signal according to the evaporation and condensation conditions and the third standard condition includes: If the condenser inlet pressure is greater than the first preset pressure, generating a refrigerant excess signal and / or a condenser blockage signal; and If the condenser outlet pressure is less than the second preset pressure, generating a refrigerant leakage signal and / or an expansion valve opening abnormal signal.

9. The fault diagnosis method for the water-cooled precision air-conditioning unit according to claim 1, characterized in that The obtaining of the evaporation and condensation conditions of the evaporator and the condenser includes: Adjusting the air-conditioning unit to the set standard condition and operating for a time longer than the preset standard duration; Controlling the thermal imager to be perpendicular to the condenser surface at a preset distance, and collecting the temperature data of the heat exchanger tube bundle area; and Dividing the heat exchanger tube bundle area into multiple cell areas; The generating of the corresponding evaporation and condensation prompt signal according to the evaporation and condensation conditions and the third standard condition includes: If the temperature change of adjacent cell regions is greater than a preset temperature difference gradient, a refrigerant cross-leakage signal is generated; A first quantity of the cell regions are formed into a test region. If the temperature difference of the test region is greater than a preset local temperature difference, a refrigerant cross-leakage signal is generated; and If the standard deviation of the temperature data of the entire heat exchanger tube bundle region is greater than a third preset temperature difference, a refrigerant cross-leakage signal is generated.

10. The fault diagnosis method for a water-cooled precision air conditioner unit according to claim 1, wherein comparing the occurrence times of the compression anomaly signal, the mechanical fault signal, and the evaporation and condensation prompt signal, and calculating the occurrence correlation quantity between the signals includes: within the historical preset duration, if two or more signals occur simultaneously within a preset unit duration, the corresponding signals are counted as one occurrence correlation quantity; generating signal risk assessment data according to the occurrence correlation quantity includes: generating corresponding fault risk levels according to the corresponding occurrence correlation quantities of the compression anomaly signal, the mechanical fault signal, and the evaporation and condensation prompt signal.

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