Intelligent health status assessment method for building mechanical and electrical equipment

Abstract

The present invention provides an intelligent health status assessment method for building mechanical and electrical equipment, which relates to the technical field of equipment status assessment. The present invention constructs a temperature model by acquiring the temperature curve of the monitored equipment at different powers, acquires the predicted temperature curve according to the next task and analyzes it to obtain the predicted temperature score, acquires the previous vibration curve of the monitored equipment for analysis and obtains the current vibration score, acquires historical tasks similar to the next task and obtains historical temperature scores and historical vibration scores, obtains health scores in combination with intermediate tasks, and analyzes the health level. The present invention generates a health score by linking the next task with the actual physical state and historical state of the equipment, thereby ensuring the accuracy of the equipment health status assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment status assessment, and in particular to an intelligent health status assessment method for building electromechanical equipment. Background Art

[0002] Building mechanical and electrical equipment plays an indispensable role in modern architecture, encompassing multiple critical subsystems. The efficient operation of this equipment not only directly impacts building comfort and safety but also significantly influences energy consumption and operating costs. With the development of smart buildings and IoT technologies, the demand for automated monitoring and management of building mechanical and electrical equipment is increasing. This enables real-time monitoring, early warning, and optimized control of equipment operating status, thereby improving overall building management efficiency and user experience.

[0003] Currently, health assessments of building mechanical and electrical equipment primarily rely on the monitoring and analysis of basic parameters such as temperature and vibration. However, traditional methods have limitations when processing multi-dimensional, dynamically changing operational data, making it difficult to fully and accurately reflect the actual operating status of the equipment. In particular, under varying operating power and mission durations, the temperature curves and vibration characteristics of equipment are complex and variable, making it difficult to effectively predict and assess the health of equipment using a single state or simple model.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent health status assessment method for building electromechanical equipment to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The intelligent health status assessment method for building mechanical and electrical equipment includes the following specific steps:

[0008] Step 1: Obtain the temperature curve of the monitored device at different operating powers. Use the operating power and task duration of the temperature curve as the training set, train with the corresponding temperature curve labels, obtain the temperature model, obtain the required operating power and task duration of the monitored device, input them into the temperature model, and obtain the predicted temperature curve.

[0009] Step 2: Analyze the predicted temperature curve, obtain the over-temperature rate through the temperature threshold, obtain the temperature change rate through the benchmark temperature change rate, obtain the fluctuation rate and accumulated heat load, and form a predicted temperature score;

[0010] Step 3: Obtain the vibration curve of the monitored device during the last mission and the vibration curve during the normal mission. Obtain the vibration curve of the device during the normal mission. Combined with the vibration curve during the normal mission, obtain the amplitude deviation, stable volatility, stable time ratio and impact attenuation of the vibration curve during the last mission. The impact attenuation is obtained by the average change rate to form the current vibration score.

[0011] Step 4: Obtain historical tasks similar to the next task. The similarity is determined by similarity, and the temperature curve and vibration curve of the historical tasks are obtained. The historical temperature score is constructed by referring to the construction method of the predicted temperature score and the temperature curve of the historical tasks. The historical vibration score is constructed by referring to the construction method of the current vibration score and the vibration curve of the historical tasks.

[0012] Step 5: Obtain the power and duration of each intermediate task from the previous task to the previous task, obtain the device health score, set the health threshold, and determine the next task capability based on the health status.

[0013] Furthermore, a temperature curve of the monitored device at different operating powers is obtained, wherein the horizontal axis of the temperature curve is time and the vertical axis is temperature. The time is the time between the start of the task and the end of the task of the device, and the temperature is the temperature obtained by the temperature monitor equipped on the device itself. The temperature curve at different operating powers is obtained by conducting experiments on the same model of the monitored device.

[0014] The domain length of the power and temperature curves is used as the training set, where the domain length is the task duration required by the device. The corresponding temperature curve is used as a label, and a linear regression model is input for training to obtain a temperature model.

[0015] Furthermore, the operating power and task duration required for the next task are obtained, and the operating power and task duration are input into the temperature model to obtain a predicted temperature curve.

[0016] Furthermore, the predicted temperature curve is analyzed to obtain the temperature threshold of the monitored equipment and the over-temperature rate. The formula is as follows:

[0017]

[0018] in, is the value of the overtemperature rate, is the highest value of the predicted temperature curve, is the temperature threshold, is the coefficient, ;

[0019] To obtain the volatility, the logic is as follows:

[0020] Get the mean and standard deviation of the predicted temperature curve and the volatility according to the following formula:

[0021]

[0022] in, is the volatility value, is the standard deviation of the predicted temperature curve, is the average value of the predicted temperature curve, The duration of the next task, The duration of high temperature;

[0023] The high temperature duration is the duration during which the predicted temperature curve value exceeds 80% of the temperature threshold.

[0024] Furthermore, to obtain the temperature change rate, the logic is as follows:

[0025] Obtain the rate of change curve and the average rate of change curve of the predicted temperature curve, obtain the baseline temperature change rate of the monitored equipment, determine the maximum absolute value of the rate of change curve, and construct the temperature change rate based on the following formula:

[0026]

[0027] in, is the value of the temperature change rate, Indicates the selection of the maximum function, is the maximum absolute value of the rate of change curve, is the average rate of change, is the base temperature change rate;

[0028] Get the cumulative heat load, the logic is as follows:

[0029]

[0030] in, is the value of the cumulative heat load, To predict the temperature curve, is the temperature threshold, The duration of the next task;

[0031] Construct the predicted temperature score based on the following formula:

[0032]

[0033] in, To score the predicted temperature, is the over-temperature rate, is the volatility, is the temperature change rate, is the cumulative heat load, 、 、 and is the coefficient, .

[0034] Furthermore, the vibration curve of the monitored equipment during a single task is obtained, and the vibration curve of the equipment during normal tasks is obtained. The normal task is the operation process of a specific working time under a specific operating power calibrated by the merchant. The amplitude range of the equipment during normal tasks is obtained. The horizontal axis of the vibration curve is time and the vertical axis is amplitude. The current vibration score is constructed based on the following formula:

[0035]

[0036] in, Score the current vibration, is the amplitude deviation, To stabilize volatility, is the proportion of stable time, is the impact attenuation;

[0037] The logic for obtaining amplitude deviation is as follows:

[0038]

[0039] in, is the amplitude deviation, represents the maximum selection function, is the maximum amplitude of the vibration curve during the last mission, is the maximum value of the amplitude range during normal tasks;

[0040] The logic for obtaining stable volatility is as follows:

[0041]

[0042] in, To stabilize volatility, represents the maximum selection function, is the standard deviation of the vibration curve during the last mission, is the standard deviation of the vibration curve during normal mission.

[0043] Furthermore, the logic for obtaining the stable time ratio is as follows:

[0044]

[0045] in, is the proportion of stable time, For a reasonable time, is the task duration;

[0046] The reasonable time is the duration during which the amplitude of the vibration curve during the last task is within the amplitude range during the normal task;

[0047] The logic for obtaining the impact attenuation is as follows:

[0048]

[0049] in, is the impact attenuation, represents the maximum selection function, is the number of impact events detected in the vibration curve during the last mission, The baseline number of impacts during normal tasks;

[0050] The logic for obtaining the number of impact events is as follows:

[0051] The vibration curve is divided into multiple curve segments according to equal time, and the average change rate of each curve segment is obtained respectively. The formula is as follows:

[0052]

[0053] in, For the The rate of change of the amplitude of the curve segment, For the The end amplitude of the curve segment, For the The initial amplitude of the curve segment, is the time length of the curve segment, Retrieve variables for curve segment times, , , is the number of curve segments;

[0054] Obtain a change rate threshold, and obtain the number of curve segments whose amplitude change rate exceeds the change rate threshold, which is the number of impact events.

[0055] Furthermore, historical tasks similar to the next task are obtained. The historical tasks are obtained through work logs, and the similarity is determined by the running power and task duration. The similarity is based on the following formula:

[0056]

[0057] in, is the similarity, is the operating power for the next mission, is the operating power of the historical task, The duration of the next task, The duration of the historical mission;

[0058] Select the historical task with the highest similarity, and obtain the temperature curve and vibration curve of the historical task. Build a historical temperature score based on the predicted temperature score and the temperature curve of the historical task. Build a historical vibration score based on the current vibration score and the vibration curve of the historical task.

[0059] Furthermore, the power and task duration of each intermediate task from the historical task to the previous task are obtained. The intermediate tasks are obtained through work logs, and a health score is constructed based on the following formula:

[0060]

[0061] in, Score your health, To score the predicted temperature, Score historical temperatures. Score the current vibration, Score historical vibrations, Indicates the The operating power of the intermediate tasks, Indicates the The duration of the intermediate tasks, Retrieve variables for intermediate task numbers, , , The total number of director tasks;

[0062] Set health thresholds and compare health scores with health thresholds:

[0063] When the health score is less than the health threshold, it means that the current monitored device status can meet the next task;

[0064] When the health score is greater than the health threshold, adjust the power and duration of the device participating in this task, obtain a new predicted temperature curve, and repeat steps 2 to 5 until the health score is less than the health threshold. If the health score cannot be less than the health threshold, an alarm is issued.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] The present invention obtains the required power and task duration according to the next task amount and inputs the trained temperature model to obtain a predicted temperature curve, analyzes the predicted temperature curve to obtain a predicted temperature score, obtains the vibration curve of the equipment's previous task and analyzes it to obtain the current vibration score, obtains similar historical tasks according to the next task, and judges the temperature curve and vibration curve of the historical tasks to obtain the historical temperature score and the historical vibration score, obtains the working history from the historical task to the previous task, generates a health score and guides the operation. The present invention generates a health score by linking the next task with the actual physical state of the equipment, and referring to similar historical tasks, comparing the states of the historical tasks, the previous task and the next task, thereby ensuring the accuracy of the equipment health status assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 Schematic diagram of the overall method of the present invention. DETAILED DESCRIPTION

[0068] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0069] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0070] Example:

[0071] See also Figure 1 , the present invention provides a technical solution:

[0072] Step 1: Obtain the temperature curve of the monitored device at different operating powers. Use the operating power and task duration of the temperature curve as the training set, train with the corresponding temperature curve labels, obtain the temperature model, obtain the required operating power and task duration of the monitored device, input them into the temperature model, and obtain the predicted temperature curve.

[0073] The step 1 includes the following:

[0074] Step 101: Obtain a temperature curve of the monitored device at different operating powers, where the horizontal axis of the temperature curve represents time and the vertical axis represents temperature. The time represents the time between the start of a task and the completion of the task, and the temperature represents the temperature obtained by a temperature monitor equipped on the device itself. The temperature curves at different operating powers are obtained by conducting experiments on devices of the same model as the monitored device.

[0075] The domain length of the power and temperature curves is used as the training set, where the domain length is the task duration required by the device. The corresponding temperature curve is used as a label, and a linear regression model is input for training to obtain a temperature model.

[0076] This step experimentally captures the device's temperature curves at different operating powers, comprehensively reflecting the device's temperature variations under various power conditions. Accurately capturing these temperature curves provides high-quality foundational data for subsequent temperature model training, ensuring the model accurately captures the device's thermal characteristics at varying power levels. Furthermore, data obtained through standardized experimental methods enhances the model's versatility and reliability, facilitating effective predictions in real-world applications.

[0077] Step 102: Obtain the operating power and task duration required for the next task, input the operating power and task duration into a temperature model, and obtain a predicted temperature curve.

[0078] This step, by inputting the required operating power and mission duration into the trained temperature model, can predict the device's temperature trends during the next mission. This prediction not only helps operators identify potential overheating risks in advance but also provides estimated data for subsequent temperature analysis and scoring, optimizing the feedforward control capabilities of the entire monitoring process and ensuring safe and efficient equipment operation.

[0079] Step 2: Analyze the predicted temperature curve, obtain the over-temperature rate through the temperature threshold, obtain the temperature change rate through the benchmark temperature change rate, obtain the fluctuation rate and accumulated heat load, and form a predicted temperature score;

[0080] The step 2 includes the following:

[0081] Step 201: Analyze the predicted temperature curve to obtain the temperature threshold of the monitored equipment and the over-temperature rate according to the following formula:

[0082]

[0083] in, is the value of the overtemperature rate, is the highest value of the predicted temperature curve, is the temperature threshold, is the coefficient, .

[0084] By reasonably defining and adjusting the relationship between various variables, the over-temperature risk of the equipment can be accurately quantified, providing a scientific basis for the assessment of the thermal health status of the equipment, ensuring that potential overheating problems can be identified and prevented in a timely manner in subsequent steps, and ensuring the safe and stable operation of the equipment. The increase, Increasing exponentially means that the closer the device temperature gets to or exceeds the threshold, the higher the overtemperature rate increases, reflecting a higher risk of overheating. Increase, that is, increase the upper limit of the safe temperature of the equipment. Down, will decrease, indicating that the device is still considered safe to operate at higher temperatures. The existence of It is more sensitive to changes in temperature ratio, which enhances the responsiveness of the formula to over-temperature conditions.

[0085] By analyzing the predicted temperature curve and calculating the overtemperature rate, we can accurately assess whether the equipment is at risk of exceeding safe temperature thresholds during a mission. This metric helps promptly identify potential overheating issues and prevent damage or performance degradation to the equipment due to excessive temperatures. Furthermore, the overtemperature rate calculation provides essential reference for subsequent fluctuations and temperature change rates, ensuring that the temperature score fully reflects the thermal stress state of the equipment. As a key component of the temperature score, the overtemperature rate enhances the sensitivity and accuracy of the overall assessment system, effectively ensuring the safety and reliability of equipment operation.

[0086] Step 202: Obtain the volatility. The logic is as follows:

[0087] Get the mean and standard deviation of the predicted temperature curve and the volatility according to the following formula:

[0088]

[0089] in, is the volatility value, is the standard deviation of the predicted temperature curve, is the average value of the predicted temperature curve, The duration of the next task, The duration of high temperature;

[0090] By combining the magnitude of temperature fluctuations and the duration of high temperatures, we can comprehensively assess a device's temperature stability and potential thermal instabilities during operation. This not only helps identify potential temperature fluctuation risks during operation but also provides important data support for subsequent predictive temperature scoring, ensuring the overall solution delivers greater accuracy and reliability in assessing a device's thermal health, thereby improving operational safety and efficiency.

[0091] It specifically reflects the degree of temperature fluctuation of the equipment during the forecast task, taking into account the relative volatility of the temperature curve and the duration of the high temperature state. It represents the standard deviation of the predicted temperature curve, which measures the severity of the temperature change; It is the average value of the predicted temperature curve, which is used to standardize the fluctuation amplitude and reflects the overall stability of the temperature; Indicates the duration that the temperature exceeds 80% of the temperature threshold. is the total duration of the mission, measuring the proportion of high temperature state during the entire mission. The increase, Linear increase indicates that the temperature fluctuation increases; on the contrary, The increase will lead to It decreases linearly, indicating that at higher mean temperatures, the relative volatility decreases. The increase will also increase linearly , reflecting the extension of the duration of high temperature state, while The increase of , which means that the proportion of high temperature state in longer task duration is reduced.

[0092] The high temperature duration is the duration during which the predicted temperature curve value exceeds 80% of the temperature threshold.

[0093] The volatility rate measures the stability of the temperature curve, providing information on the consistency of the device's temperature changes during a mission. A high volatility rate may indicate unstable factors during device operation, requiring timely action to ensure stable temperature control. The volatility rate not only reflects the device's temperature stability but also provides an important quantitative indicator for the overall temperature score. This metric enables the temperature score to more comprehensively assess the device's thermal dynamics, improving the overall scientificity of the assessment. Furthermore, the volatility rate data supports the subsequent calculation of the health score, ensuring stable operation and healthy status of the device throughout each mission.

[0094] Step 203: Obtain the temperature change rate. The logic is as follows:

[0095] Obtain the rate of change curve and the average rate of change curve of the predicted temperature curve, obtain the baseline temperature change rate of the monitored equipment, determine the maximum absolute value of the rate of change curve, and construct the temperature change rate based on the following formula:

[0096]

[0097] in, is the value of the temperature change rate, Indicates the selection of the maximum function, is the maximum absolute value of the rate of change curve, is the average rate of change, is the base temperature change rate.

[0098] It specifically reflects the rate of temperature change of the device during the forecasting task, and measures the severity of the device temperature fluctuation and the overall change trend. Indicates the absolute maximum value of the temperature change rate curve, is the average value of the temperature change rate, is the benchmark temperature change rate, used for calculating the standardized temperature change rate. The function selects the larger of the two calculated values to ensure that the temperature change rate fully reflects the most extreme conditions of the device during temperature changes. Depends on the larger of the ratio of the maximum absolute value of the temperature change rate to the reference temperature change rate, and the ratio of its average temperature change rate to the reference temperature change rate. When the peak value of the temperature change rate is significantly higher than the reference temperature change rate, will be determined primarily by the peak value and vice versa. The increase, If the temperature of the equipment increases linearly, it means that the equipment has experienced more drastic temperature changes during operation, and the potential risk of thermal stress also increases. Increase, and The growth rate is not large. will also increase accordingly, but the magnitude depends on Relative to changes. As the benchmark temperature change rate, it plays the role of standardizing the temperature change rate. The value will decrease and , thereby reducing The temperature change rate can be flexibly adjusted according to the specific operating characteristics and safety standards of the equipment, improving the accuracy and applicability of the assessment.

[0099] The temperature variability (TDR) analyzes the rate of temperature change to assess the smoothness and responsiveness of equipment during a mission. A high TDR may indicate a potential risk of overheating during rapid temperature changes, necessitating measures to prevent equipment failures caused by excessive temperature fluctuations. The TDR assessment not only enhances the sophistication of temperature scoring but also provides more specific guidance for equipment thermal management. The introduction of this metric makes the temperature scoring system more comprehensive, more accurately reflecting equipment performance under varying temperature variations and promoting optimization and improvement in actual operation.

[0100] Step 204: Obtain the accumulated heat load. The logic is as follows:

[0101]

[0102] in, is the value of the cumulative heat load, To predict the temperature curve, is the temperature threshold, The duration of the next task;

[0103] It specifically reflects the total heat accumulation degree that the equipment is subjected to during the entire mission, and measures the heat accumulation level suffered by the equipment during long-term operation. The overall increase in the heat accumulated by the equipment during the mission leads to an increase in The increase reflects that the heat load on the equipment has increased, and the potential heat risk has also increased. The increase in the means that the device can withstand higher temperatures without being considered overheated, so that under the same heat accumulation, will decrease, indicating that the heat load pressure of the equipment is relatively reduced. The increase in will dilute the effect of heat accumulation, because the total task duration becomes longer and the heat accumulation is spread over a longer period of time, resulting in This means that the thermal load borne by the equipment per unit time is relatively low during longer missions. The calculation of not only reflects the heat accumulation of equipment during long-term operation but also provides a key quantitative basis for predicting equipment status. This metric plays a crucial role in linking temperature profiles with equipment health scores within the overall solution, ensuring that equipment effectively manages heat loads during continuous operation, preventing equipment failure or performance degradation due to accumulated overheating, and thus improving operational safety and reliability.

[0104] Cumulative heat load reflects the total heat load experienced by equipment during a mission by accumulating temperatures over the entire mission. This metric helps assess the thermal effects experienced by equipment during long-term operation and prevent the risk of shortened equipment lifespan or failure due to cumulative overheating. The calculation of cumulative heat load not only provides a comprehensive view of the thermal load on equipment during operation but also provides key data support for the comprehensive evaluation of temperature scores. The introduction of this metric enhances understanding of long-term thermal stress on equipment, facilitates the development of more appropriate maintenance strategies and operating parameters, and improves equipment reliability and lifespan.

[0105] Step 205: Construct a predicted temperature score based on the following formula:

[0106]

[0107] in, To score the predicted temperature, is the over-temperature rate, is the volatility, is the temperature change rate, is the cumulative heat load, 、 、 and is the coefficient, .

[0108] It specifically reflects the overall temperature health status of the equipment. By comprehensively considering four temperature-related indicators: over-temperature rate, fluctuation rate, temperature change rate and cumulative heat load, it quantifies the temperature performance and health status of the equipment during operation. 、 、 and The sum of these weight coefficients ensures that the contribution of different indicators to the final score can be flexibly adjusted according to actual needs. 、 、 or When the value of any one of the indicators increases, the value of the weighted sum will also increase, making This means that the thermal health of the device deteriorates as these indicators deteriorate, and vice versa. 、 、 or When the value of decreases, the weighted sum decreases, resulting in increases, indicating that the thermal health of the device is improving. 、 、 or and The relationship is negatively correlated, and an exponential function is used. right 、 、 or The changes in have nonlinear sensitivity. It can capture subtle changes in temperature indicators more delicately, thus providing a more accurate and dynamic temperature score. Indicates a higher risk of overtemperature and will significantly reduce , reflecting the serious overheating problem that the equipment may face; and higher This indicates that the device performs well in temperature management and the risk is low.

[0109] By comprehensively analyzing the over-temperature rate, fluctuation rate, temperature change rate, and cumulative heat load, a predictive temperature score is constructed, achieving a multi-dimensional, comprehensive equipment thermal health assessment. The predictive temperature score not only reflects the overall temperature status of the equipment during the mission, but also identifies potential thermal risks and unstable factors. The establishment of this scoring mechanism makes the equipment's operational evaluation more quantitative and systematic, providing a reliable basis for subsequent health scores and mission capability judgments. At the same time, the introduction of a comprehensive score ensures the coordinated consideration of various temperature indicators, improves the scientific nature and accuracy of the overall assessment system, and promotes the organic connection and coordinated operation between the various steps of the entire plan.

[0110] Step 3: Obtain the vibration curve of the monitored device during the last mission and the vibration curve during the normal mission. Obtain the vibration curve of the device during the normal mission. Combined with the vibration curve during the normal mission, obtain the amplitude deviation, stable volatility, stable time ratio and impact attenuation of the vibration curve during the last mission. The impact attenuation is obtained by the average change rate to form the current vibration score.

[0111] By comparing the vibration curve of the previous mission with the vibration curve of a normal mission, the current operating status and stability of the equipment can be comprehensively assessed. The construction of the vibration score not only reflects the vibration characteristics of the equipment in actual missions, but also identifies potential mechanical anomalies and failure risks. This scoring mechanism provides a quantitative basis for vibration monitoring and health assessment of the equipment, ensuring dynamic monitoring and timely maintenance of the equipment during operation. At the same time, the data support of the vibration score provides supplementary information for subsequent health scoring and mission capability assessment, promotes the synergy of dual temperature and vibration monitoring in the overall solution, and improves the overall efficiency of equipment management.

[0112] The step 3 includes the following:

[0113] Step 301: Obtain the vibration curve of the monitored device during a single task, obtain the vibration curve of the device during normal task, wherein the normal task is the operation process of the device under a specific operating power and a specific working time as calibrated by the merchant, obtain the amplitude range of the device during normal task, wherein the horizontal axis of the vibration curve is time and the vertical axis is amplitude, and construct the current vibration score according to the following formula:

[0114]

[0115] in, Score the current vibration, is the amplitude deviation, To stabilize volatility, is the proportion of stable time, is the impact attenuation;

[0116] Reflects the overall vibration health of the equipment during the current mission. By calculating the average value of the four vibration indicators, it quantifies the vibration performance of the equipment during operation. This means that each indicator has equal importance in the final score. It ensures that different vibration characteristics contribute to the overall score in a balanced manner and avoids excessive influence of a single indicator on the score result. There is a positive correlation. Increase, that is, the greater the equipment vibration amplitude deviates from the normal range, It also increases, reflecting the increased risk of abnormal equipment vibration. and There is also a positive correlation. This indicates that the vibration fluctuation is unstable and there are more irregular vibrations during the operation of the equipment, which will lead to Increased, indicating that the device may be unstable or have potential failures. also with is positively correlated. Increase, indicating that the device maintains a stable vibration state for a longer period of time, Therefore, it increases, reflecting that the equipment operates more smoothly. and There is a positive correlation. This indicates that the equipment can more effectively mitigate vibration shock. Increased, the display device has good vibration resistance and more reliable operation.

[0117] By obtaining vibration curves from the previous mission and normal missions, a comparative baseline is provided for constructing the vibration score. This comparative analysis effectively identifies vibration anomalies in the equipment during the previous mission and promptly detects potential mechanical failures or performance degradation. The establishment of a baseline vibration curve ensures the objectivity and standardization of the scoring system, making the vibration score highly comparable and reliable. Furthermore, the acquisition of the vibration curve provides the necessary data support for the calculation of subsequent vibration indicators, ensuring the accuracy and comprehensiveness of the overall vibration score and improving the refinement of equipment vibration monitoring.

[0118] Step 302: The amplitude deviation acquisition logic is as follows:

[0119]

[0120] in, is the amplitude deviation, represents the maximum selection function, is the maximum amplitude of the vibration curve during the last mission, is the maximum value of the amplitude range during normal tasks;

[0121] It reflects the degree of deviation of the vibration amplitude of the equipment during the current task relative to the reference amplitude range, and measures the abnormality of the equipment's vibration performance. Function ensures The value of will not be negative, thus maintaining the non-negativity of the deviation. By comparing the maximum amplitude of the current task with the maximum value of the benchmark amplitude, the deviation of the device vibration amplitude is quantified. When the device vibration amplitude deviates significantly from the benchmark range, will approach 0, reflecting a higher risk of vibration anomaly; and when Only slightly higher than Or below the benchmark value, The higher the value, the smaller the vibration deviation is, and the more stable the equipment operation is.

[0122] By comparing the maximum amplitude of the current task's vibration curve with the baseline amplitude, the amplitude deviation can intuitively reflect whether the equipment is experiencing abnormal vibration during operation. This indicator helps to promptly detect early signs of equipment performance degradation or mechanical component wear, preventing major failures caused by abnormal vibration. The acquisition of amplitude deviation not only enhances the sensitivity of the vibration score but also provides an important quantitative basis for the comprehensive vibration score. The introduction of this indicator enables the vibration score to more accurately assess the operational stability of the equipment, ensuring the health and reliability of the equipment in long-term operation, and providing solid support for the health assessment of the overall solution.

[0123] Step 303: The logic for obtaining stable volatility is as follows:

[0124]

[0125] in, To stabilize volatility, represents the maximum selection function, is the standard deviation of the vibration curve during the last mission, is the standard deviation of the vibration curve during normal mission.

[0126] Reflects the consistency and stability of the equipment vibration data. The higher the value, the more stable the vibration fluctuation of the equipment during operation, the closer the fluctuation amplitude is to the baseline state, and the smoother the operation. It reflects the vibration fluctuation of the equipment during this mission, while the benchmark standard deviation represents the vibration fluctuation level of the equipment under normal mission conditions. and and There is an inverse relationship. When the vibration standard deviation of the current task increases, the score Increase, leading to Reduce, thus Decreases, indicating that the vibration fluctuation of the equipment has become worse. On the contrary, when When decreasing, Reduce, Increase, leading to The increase reflects that the vibration fluctuation of the equipment is more stable. In addition, if Greater than or equal to 1, then is a negative value, The function ensures that the value is non-negative and is at least 0. This ensures that the value is within a reasonable range and accurately reflects the stability of the device's operation. Stability Fluctuation provides a quantitative assessment of the device's operational stability by comparing the vibration fluctuation amplitude of the current task with that of the baseline task. This helps identify whether unstable vibration factors exist during device operation, thereby ensuring reliable operation.

[0127] Stability volatility assesses the stability of equipment operation by comparing the standard deviation of the vibration curves between the current task and the baseline task. A lower stability volatility indicates that the equipment maintains good stability during operation, reducing the probability of potential failures. This metric not only reflects the vibration characteristics of the equipment under normal operating conditions but also identifies unstable factors in the equipment during specific tasks. The introduction of stability volatility improves the comprehensiveness and accuracy of vibration scoring, enabling the scoring system to more comprehensively reflect the operating status of the equipment. The acquisition of this metric provides a basis for stability assessment for subsequent health scoring and mission capability assessment, ensuring the continued stable operation of the equipment in various tasks.

[0128] Step 304: The logic for obtaining the stable time ratio is as follows:

[0129]

[0130] in, is the proportion of stable time, For a reasonable time, is the task duration;

[0131] It reflects the degree to which the equipment maintains a stable operating state during the entire task. The higher the value, the greater the proportion of time the equipment maintains the vibration amplitude within the normal range, and the smoother the operation. The ratio of the reasonable time to the total time is calculated. It can evaluate the effectiveness of the equipment in maintaining a stable vibration state during the mission. The longer the reasonable time is, the more stable the equipment is in operation. Conversely, it means that the equipment has abnormal vibration for a greater proportion of the time. It also increases, indicating that the equipment maintains a stable vibration state for a longer time and operates more smoothly. On the contrary, when the total task duration increases and the reasonable time remains unchanged, will decrease, indicating that the equipment has a relatively shorter time in a stable state during a longer mission, and there may be more vibration anomalies. and At the same time, but The growth rate is faster than ,but will still increase and vice versa. The value accurately reflects the operational stability and temporal performance of the device during the mission. Comparing the ratio of the time the device maintains stable vibration to the total mission time provides a quantitative assessment of the device's operational stability. This helps identify whether the device can maintain a good operating state throughout the mission, thereby ensuring device reliability and performance and avoiding potential failures or performance degradation caused by unstable vibration.

[0132] The reasonable time is the duration during which the amplitude of the vibration curve during the last task is within the amplitude range during the normal task;

[0133] The Stable Time Percentage (STP) evaluates the operational stability and reliability of the equipment during a mission by calculating the proportion of time that the equipment's vibration remains within the normal range. A higher STP indicates that the equipment has maintained good operating conditions for a longer period of time, reducing the likelihood of failure. This metric not only reflects the equipment's ongoing stability during the mission but also provides a temporal basis for evaluating the vibration score. The introduction of STP makes the vibration score more comprehensive and detailed, more accurately reflecting the equipment's operating status. Obtaining this metric facilitates the subsequent calculation of the health score and assessment of mission capability, ensuring the equipment's continued efficient and stable operation across various missions.

[0134] Step 305: The logic for obtaining the impact attenuation is as follows:

[0135]

[0136] in, is the impact attenuation, represents the maximum selection function, is the number of impact events detected in the vibration curve during the last mission, The baseline number of impacts during normal tasks;

[0137] This value reflects the relationship between the number of sudden vibrations (i.e., shock events) encountered by the device during operation and the number of shock events in the baseline mission. A higher value indicates a greater ability of the device to attenuate shock events. This means fewer shock events occurred during the current mission, indicating good vibration resistance and rapid recovery capabilities. When it increases, it means that more impact events are detected in the current task, resulting in Increase, thus Reduce, eventually This indicates that the device has a weak ability to attenuate shock events in the current task. When reducing, Reduce, Increase, leading to This indicates that the device has a strong ability to attenuate shock events in the current task. Function, ensure It always stays in the non-negative range, providing a stable and reliable basis for evaluation. It plays a key role in equipment health scoring, helping to identify whether the equipment has sufficient vibration resistance and prevent the risk of mechanical damage or failure due to excessive vibration.

[0138] The logic for obtaining the number of impact events is as follows:

[0139] The vibration curve is divided into multiple curve segments according to equal time, and the average change rate of each curve segment is obtained respectively. The formula is as follows:

[0140]

[0141] in, For the The rate of change of the amplitude of the curve segment, For the The end amplitude of the curve segment, For the The initial amplitude of the curve segment, is the time length of the curve segment, Retrieve variables for curve segment times, , , is the number of curve segments;

[0142] It reflects the severity of vibration fluctuations and the dynamic characteristics of equipment response. A value of 0 indicates a large amplitude change within a short period of time, which may indicate that the equipment has experienced significant vibration shock or dynamic loads during that period.

[0143] Obtain a change rate threshold, and obtain the number of curve segments whose amplitude change rate exceeds the change rate threshold, which is the number of impact events.

[0144] The shock attenuation evaluates the equipment's response and mitigation capabilities to sudden vibrations during operation by analyzing the number of shock events in the vibration curve. A higher shock attenuation indicates that the equipment has good vibration resistance and can effectively mitigate potential damage to the equipment structure caused by vibration. This indicator not only reflects the dynamic response characteristics of the equipment, but also identifies possible vibration control problems that may exist during the operation of the equipment. The introduction of shock attenuation enriches the dimensions of vibration scoring, allowing the scoring system to more comprehensively evaluate the performance of equipment under different vibration conditions. The acquisition of this indicator provides key data support for the comprehensive evaluation of vibration scores, ensuring that the equipment can maintain a good operating state under various working conditions, and provides an important basis for the health assessment of the overall solution and the judgment of mission capabilities.

[0145] Step 4: Obtain historical tasks similar to the next task. The similarity is determined by similarity, and the temperature curve and vibration curve of the historical tasks are obtained. The historical temperature score is constructed by referring to the construction method of the predicted temperature score and the temperature curve of the historical tasks. The historical vibration score is constructed by referring to the construction method of the current vibration score and the vibration curve of the historical tasks.

[0146] The step 4 includes the following contents:

[0147] Obtain historical tasks similar to the next task. The historical tasks are obtained from work logs. The similarity is determined by running power and task duration. The similarity is based on the following formula:

[0148]

[0149] in, is the similarity, is the operating power for the next mission, is the operating power of the historical task, The duration of the next task, The duration of the historical mission;

[0150] This value reflects how close the next mission is to previous missions in terms of power and duration. A higher value indicates that the two missions are more similar in these two key parameters, and the more relevant and effective the selected historical mission is as a reference.

[0151] and The relative differences between the next task and the historical tasks in terms of operating power and task duration are measured respectively. The similarity of these two dimensions is comprehensively represented, ensuring that the similarity evaluation takes into account both the consistency of power and the matching of task duration, thus fully reflecting the similarity of tasks. near hour, Approaching 1, near hour, Also approaches 1. When the running power and task duration of the next task are highly close to those of the historical tasks, both ratios are close to 1. is also close to 100%, indicating that the tasks are highly similar. or When increasing or decreasing, Respond to changes proportionally. Increasing or decreasing the ratio directly affects By taking the average of the ratio, the extreme change in one dimension will be mitigated by the relative stability of the other dimension. The similarity between the two tasks in terms of these two key parameters is quantified. The selection of similar historical tasks in step 4 provides a quantitative basis to ensure the relevance and validity of the reference data, thereby optimizing the process of constructing equipment temperature and vibration scores.

[0152] Select the historical task with the highest similarity, and obtain the temperature curve and vibration curve of the historical task. Build a historical temperature score based on the predicted temperature score and the temperature curve of the historical task. Build a historical vibration score based on the current vibration score and the vibration curve of the historical task.

[0153] By acquiring historical mission data similar to the next mission and constructing corresponding historical temperature and vibration scores, a valuable reference basis can be provided for the equipment's upcoming mission. This process not only ensures the relevance and validity of the reference data through similarity judgment, but also optimizes the prediction and evaluation of the current mission by utilizing the temperature and vibration scores in the historical data. The introduction of historical temperature scores enables the temperature and vibration scores of the current mission to be compared with historical performance, identifying trends and potential problems in equipment operation. The implementation of this step enhances the predictive capabilities and evaluation depth of the overall solution, ensuring that the operating parameters and health status of the equipment in the new mission can be more accurately predicted and optimized, and improving the scientific nature and foresight of equipment management.

[0154] Step 5: Obtain the power and duration of each intermediate task from the previous task to the previous task, obtain the device health score, set the health threshold, and determine the next task capability based on the health status.

[0155] The step 5 includes the following contents:

[0156] Obtain the power and task duration of each intermediate task from the previous task to the previous task. The intermediate tasks are obtained through work logs and a health score is constructed based on the following formula:

[0157]

[0158] in, Score your health, To score the predicted temperature, Score historical temperatures. Score the current vibration, Score historical vibrations, Indicates the The operating power of the intermediate tasks, Indicates the The duration of the intermediate tasks, Retrieve variables for intermediate task numbers, , , The total number of director tasks;

[0159] By combining the temperature score and vibration score with the operating power and task duration of the intermediate tasks, the health status of the equipment is quantified. This indicates that the equipment has performed well in terms of temperature and vibration, and has operated with low thermal loads in historical missions, and vice versa. This score provides a scientific basis for determining whether the equipment is suitable for the next mission, ensuring that the equipment is in good health before continuing operation and avoiding potential failure risks. It represents the ratio of the predicted temperature score of the next task to the temperature score of the historical tasks. Higher than , then the ratio is greater than 1, indicating that the temperature health status of the next task is lower than that of the historical tasks, and vice versa. It represents the ratio of the vibration score of the last mission to the vibration score of the historical mission. Higher than , then the ratio is greater than 1, indicating that the vibration health status of the current task is lower than that of the historical tasks, and vice versa. Indicates the degree of consumption of the device health status by each task in the middle. The more tasks there are, the greater the running power and the longer the working time, the greater the consumption of the device health status. Indicates the reduction of intermediate task data to avoid the denominator value being too large. By comparing the ratio of the numerator and denominator, the numerator reflects the degree of deviation of the health status of the next task and the previous task compared with the historical task, and the numerator reflects the consumption of the equipment health by the intermediate task. When the degree of deviation of the health status is greater than the consumption of the equipment health, It will increase, indicating that the current health status of the device is declining. When the deviation of the health status is less than the consumption of the device health, It will decrease, indicating that the current device health status is still maintained at a high level.

[0160] Set health thresholds and compare health scores with health thresholds:

[0161] When the health score is less than the health threshold, it means that the current monitored device status can meet the next task;

[0162] When the health score is greater than the health threshold, adjust the power and duration of the device participating in this task, obtain a new predicted temperature curve, and repeat steps 2 to 5 until the health score is less than the health threshold. If the health score cannot be less than the health threshold, an alarm is issued.

[0163] By analyzing the operating power and task duration of each intermediate task between the historical task and the previous task, and calculating the equipment health score, we can comprehensively evaluate the long-term operating status and health trends of the equipment. The calculation of the health score comprehensively considers the temperature score and vibration score, reflecting the health status of the equipment in multiple dimensions. Health thresholds are set and compared to ensure that the equipment is in good health before the next task, avoiding operational risks caused by equipment fatigue or wear and tear. When the health score exceeds the threshold, the operating parameters are adjusted or maintenance is performed to ensure that the equipment can continue to participate in the task stably. This step not only provides a scientific basis for predictive maintenance of equipment, but also improves the intelligent management level of the overall solution through the introduction of health scores. Through dynamic monitoring and evaluation of health status, the efficient and safe operation of equipment in various tasks is ensured, the equipment management process is optimized, and the overall operational efficiency is improved.

[0164] By integrating temperature, voltage, and vibration scores through the cube root method, a comprehensive health score is constructed, enabling multi-dimensional health assessment. A significant decrease in any score will result in a lower overall score, triggering the threshold judgment logic.

[0165] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0166] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0167] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0168] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. An intelligent health status assessment method for building electromechanical equipment, characterized in that: The specific steps include: Step 1: Obtain the temperature curve of the monitored device at different operating powers. Use the operating power and task duration of the temperature curve as the training set, train with the corresponding temperature curve labels, obtain the temperature model, obtain the required operating power and task duration of the monitored device, input them into the temperature model, and obtain the predicted temperature curve. Step 2: Analyze the predicted temperature curve, obtain the over-temperature rate through the temperature threshold, obtain the temperature change rate through the benchmark temperature change rate, obtain the fluctuation rate and accumulated heat load, and form a predicted temperature score; Step 3: Obtain the vibration curve of the monitored device during the last mission and the vibration curve during the normal mission. Obtain the vibration curve of the device during the normal mission. Combined with the vibration curve during the normal mission, obtain the amplitude deviation, stable volatility, stable time ratio and impact attenuation of the vibration curve during the last mission. The impact attenuation is obtained by the average change rate to form the current vibration score. Step 4: Obtain historical tasks similar to the next task. The similarity is determined by similarity, and the temperature curve and vibration curve of the historical tasks are obtained. The historical temperature score is constructed by referring to the construction method of the predicted temperature score and the temperature curve of the historical tasks. The historical vibration score is constructed by referring to the construction method of the current vibration score and the vibration curve of the historical tasks. Step 5: Obtain the power and duration of each intermediate task from the previous task to the previous task, obtain the device health score, set the health threshold, and determine the next task capability based on the health status; Obtain the power and task duration of each intermediate task from the previous task to the previous task. The intermediate tasks are obtained through work logs and a health score is constructed based on the following formula: ； in, Score your health, To score the predicted temperature, Score historical temperatures. Score the current vibration, Score historical vibrations, Indicates the The operating power of the intermediate tasks, Indicates the The duration of the intermediate tasks, Retrieve variables for intermediate task numbers, , The total number of director tasks; Set health thresholds and compare health scores with health thresholds: When the health score is less than the health threshold, it means that the current monitored device status can meet the next task; When the health score is greater than the health threshold, adjust the power and duration of the device participating in this task, obtain a new predicted temperature curve, and repeat steps 2 to 5 until the health score is less than the health threshold. If the health score cannot be less than the health threshold, an alarm is issued.

2. The intelligent health status assessment method for building electromechanical equipment according to claim 1 is characterized by: Obtain a temperature curve of the monitored device at different operating powers, where the horizontal axis of the temperature curve represents time and the vertical axis represents temperature. The time represents the time between the start of the task and the completion of the task, and the temperature represents the temperature obtained by the device's own temperature monitor. The temperature curves at different operating powers are obtained by conducting experiments on devices of the same model as the monitored device. The domain length of the power and temperature curves is used as the training set, where the domain length is the task duration required by the device. The corresponding temperature curve is used as a label, and a linear regression model is input for training to obtain a temperature model.

3. The intelligent health status assessment method for building electromechanical equipment according to claim 2 is characterized by: Obtain the operating power and task duration required for the next task, input the operating power and task duration into the temperature model, and obtain the predicted temperature curve.

4. The intelligent health status assessment method for building electromechanical equipment according to claim 3 is characterized by: Analyze the predicted temperature curve to obtain the temperature threshold of the monitored equipment and the over-temperature rate. The formula is as follows: ； in, is the value of the overtemperature rate, is the highest value of the predicted temperature curve, is the temperature threshold, is the coefficient, ; To obtain the volatility, the logic is as follows: Get the mean and standard deviation of the predicted temperature curve and the volatility according to the following formula: ; in, is the volatility value, is the standard deviation of the predicted temperature curve, is the average value of the predicted temperature curve, The duration of the next task, The duration of high temperature; The high temperature duration is the duration during which the predicted temperature curve value exceeds 80% of the temperature threshold.

5. The intelligent health status assessment method for building electromechanical equipment according to claim 4 is characterized in that: Get the temperature change rate, the logic is as follows: Obtain the rate of change curve and the average rate of change curve of the predicted temperature curve, obtain the baseline temperature change rate of the monitored equipment, determine the maximum absolute value of the rate of change curve, and construct the temperature change rate based on the following formula: ； in, is the value of the temperature change rate, Indicates the selection of the maximum function, is the maximum absolute value of the rate of change curve, is the average rate of change, is the base temperature change rate; Get the cumulative heat load, the logic is as follows: ; in, The value of the cumulative heat load specifically reflects the total heat accumulation degree endured by the equipment during the entire mission, and measures the heat accumulation level suffered by the equipment during long-term operation. To predict the temperature curve, is the temperature threshold, The duration of the next task; Construct the predicted temperature score based on the following formula: ; in, To score the predicted temperature, is the over-temperature rate, is the volatility, is the temperature change rate, is the cumulative heat load, 、 、 and is the coefficient, .

6. The intelligent health status assessment method for building electromechanical equipment according to claim 5 is characterized by: Obtain the vibration curve of the monitored equipment during a single task, obtain the vibration curve of the equipment during normal tasks, where the normal task is the operation process of the equipment under a specific operating power and a specific working time as calibrated by the merchant, and obtain the amplitude range of the equipment during normal tasks. The horizontal axis of the vibration curve is time and the vertical axis is amplitude. Construct the current vibration score based on the following formula: ； in, Score the current vibration, is the amplitude deviation, To stabilize volatility, is the proportion of stable time, is the impact attenuation; The logic for obtaining amplitude deviation is as follows: ； in, is the amplitude deviation, represents the maximum selection function, is the maximum amplitude of the vibration curve during the last mission, is the maximum value of the amplitude range during normal tasks; The logic for obtaining stable volatility is as follows: ; in, To stabilize volatility, represents the maximum selection function, is the standard deviation of the vibration curve during the last mission, is the standard deviation of the vibration curve during normal mission.

7. The intelligent health status assessment method for building electromechanical equipment according to claim 6 is characterized by: The logic for obtaining the stable time ratio is as follows: ; in, is the proportion of stable time, For a reasonable time, is the task duration; The reasonable time is the duration during which the amplitude of the vibration curve during the last task is within the amplitude range during the normal task; The logic for obtaining the impact attenuation is as follows: ; in, is the impact attenuation, represents the maximum selection function, is the number of impact events detected in the vibration curve during the last mission, The baseline number of impacts during normal tasks; The logic for obtaining the number of impact events is as follows: The vibration curve is divided into multiple curve segments according to equal time, and the average change rate of each curve segment is obtained respectively. The formula is as follows: ； in, For the The rate of change of the amplitude of the curve segment, For the The end amplitude of the curve segment, For the The initial amplitude of the curve segment, is the time length of the curve segment, Retrieve variables for curve segment times, , is the number of curve segments; Obtain a change rate threshold, and obtain the number of curve segments whose amplitude change rate exceeds the change rate threshold, which is the number of impact events.

8. The intelligent health status assessment method for building electromechanical equipment according to claim 7 is characterized by: Obtain historical tasks similar to the next task. The historical tasks are obtained from work logs. The similarity is determined by running power and task duration. The similarity is based on the following formula: ; in, is the similarity, is the operating power for the next mission, is the operating power of the historical task, The duration of the next task, The duration of the historical mission; Select the historical task with the highest similarity, and obtain the temperature curve and vibration curve of the historical task. Build a historical temperature score based on the predicted temperature score and the temperature curve of the historical task. Build a historical vibration score based on the current vibration score and the vibration curve of the historical task.

Images