A method for multi-source monitoring and early fault warning of cement production equipment state

Abstract

The present application relates to the technical field of device control, and particularly relates to a cement production device state multi-source monitoring and early fault warning method, which comprises the following steps: synchronously collecting transient static pressure sequences on the upstream and downstream side walls of a flap valve, and point-by-point difference to obtain an original relative pressure difference sequence; performing a difference operation on the sequence, locking the starting boundary and ending boundary of a candidate pulse according to the difference zero-crossing condition, and back-intercepting the original waveform to calculate the spread numerical value and skewness coefficient; setting a response time window according to the feeding trigger time and the material falling transmission delay, confirming the candidate pulse in the window as an effective pulse; fusing the spread numerical value and skewness coefficient of the effective pulse, calculating a state deviation evaluation index, and comparing the index with a safety boundary threshold to realize fault warning. The present application can effectively resist low-frequency interference and air flow artifacts through joint discrimination in time and space domains and multi-dimensional morphological feature fusion, and realizes early and accurate warning of mechanical sticking or damping degradation of the flap valve.

Description

Technical Field

[0001] This invention relates to the field of equipment control technology. More specifically, this invention relates to a method for multi-source monitoring of the status of cement production equipment and early warning of faults. Background Technology

[0002] In cement production, the cyclone separator and flap valve of the preheater system operate under harsh conditions of high temperature, negative pressure, and gas-solid two-phase flow erosion. Mechanical jamming or damping degradation of the flap valve is a key hidden danger affecting the safe operation of the system. Currently, online monitoring of its operation status is usually achieved by collecting absolute pressure signals using a static pressure sensor installed on the side wall of the flap valve.

[0003] However, this monitoring method is affected by factors such as system-level airflow regulation and fluid concentration fluctuations. The sensor output generally exhibits significant common-mode baseline drift, the magnitude of which often far exceeds the high-frequency pressure changes generated by the small opening and closing actions of the flap valve. This results in the effective signal being submerged in low-frequency interference, making it difficult to achieve accurate feature extraction and state determination. Summary of the Invention

[0004] To address the technical problem that existing monitoring methods are affected by external environmental factors, resulting in the effective signals being submerged in low-frequency interference and making it difficult to achieve accurate feature extraction and state determination, this invention provides the following technical solution.

[0005] A method for multi-source monitoring of the condition and early warning of faults in cement production equipment includes:

[0006] The transient static pressure sequence of the upstream side wall and the transient static pressure sequence of the downstream side wall of the preheater cyclone discharge pipe flap valve are collected simultaneously, and the difference between the two sequences is calculated point by point to obtain the original relative pressure difference sequence. Perform a first-order time-domain difference operation on the original relative pressure difference sequence to obtain a difference sequence, and lock the start and end boundaries of the candidate pulses based on the zero-crossing condition of the difference sequence; Based on the starting and ending boundaries, local waveforms corresponding to each candidate pulse are extracted from the original relative pressure difference sequence, and the broadening value and skewness coefficient of the local waveforms are calculated. A response time window is set based on the feeding trigger time and the material falling and transporting delay. Candidate pulses whose start and end boundaries both fall within the response time window are confirmed as valid pulses. By combining the broadening value and skewness coefficient of the effective pulse, a state deviation assessment index is calculated, and the state deviation assessment index is compared with a safety boundary threshold to achieve fault early warning.

[0007] Optionally, before simultaneously acquiring the transient static pressure sequence of the upstream sidewall and the downstream sidewall of the preheater cyclone discharge pipe flap valve, the method further includes: Perform a hardware clock synchronization operation to synchronize the clocks of the upstream sidewall static pressure sensing node and the downstream sidewall static pressure sensing node.

[0008] Optionally, the data collection and subtraction process may also include: When a signal is lost on one side, the update of the original relative pressure difference sequence is frozen and the abnormal status of the probe is reported. The freeze is lifted after the signals on both sides return to normal.

[0009] Optionally, locking the start and end boundaries of the candidate pulse based on the zero-crossing condition of the difference sequence includes: When the differential sequence crosses the zero line, the crossing position is determined as the start or end boundary of the candidate pulse.

[0010] Optionally, calculating the broadening value of the local waveform includes: The time width corresponding to half of the amplitude peak in the local waveform is extracted and used as the widening value.

[0011] Optionally, the step of setting the response time window based on the feeding trigger time and the material falling and conveying delay includes: The feed trigger time is defined as the pulse control cycle trigger time of the feeder. The median of the historical material drop time difference during the initial fault-free operation phase of the equipment is taken as the material drop and transmission delay. The latency tolerance is calculated based on the standard deviation of transmission latency from historical health condition data. The start time of the response time window is equal to the feeding trigger time plus the material falling transmission delay minus the delay tolerance, and the end time of the response time window is equal to the feeding trigger time plus the material falling transmission delay plus the delay tolerance.

[0012] Optionally, the step of calculating the latency tolerance based on the transmission latency standard deviation of historical health condition data includes: Record the time difference between the feeding trigger time and the actual response time of the flap valve for each feeding action during the fault-free operation phase of the equipment, as a transmission delay sample; Calculate the standard deviation of all transmission delay samples; The delay tolerance is obtained by multiplying the standard deviation of all transmission delay samples by a preset statistical confidence factor.

[0013] Optionally, the calculation of the state deviation evaluation index by fusing the broadening value and skewness coefficient of the effective pulse includes: The average value of waveform broadening obtained statistically during the initial healthy operation cycle of the equipment is extracted as the reference broadening constant; the damping sensitivity amplification index is preset. Take the absolute value of the skewness coefficient; based on the absolute value of the skewness coefficient, the broadening value, the reference broadening constant, and the damping sensitivity amplification index, calculate the state deviation evaluation index.

[0014] Optionally, comparing the state deviation assessment index with a safety boundary threshold to achieve fault warning includes: Extract all historical data of the state deviation evaluation index calculated during the initial healthy operation cycle of the equipment, and fit the probability density distribution function of the healthy state. The upper limit of the 99% confidence level of the probability density distribution function is extracted and used as the safety boundary threshold. The real-time state deviation evaluation index calculated in each feeding cycle is compared with the safety boundary threshold. When the real-time status deviates from the evaluation index and exceeds the safety boundary threshold for three consecutive feeding cycles, an early warning command is generated.

[0015] The present invention has the following beneficial effects: 1. This invention uses point-by-point differential analysis of the transient static pressure on the upstream and downstream sidewalls of the flap valve as a preprocessing step. This directly eliminates the common-mode low-frequency drift introduced by system-level airflow regulation and fluid concentration fluctuations at the physical level, highlighting the high-frequency pressure abrupt changes caused by minute opening and closing actions. Based on this, differential zero-crossing detection is used to adaptively lock the pulse boundary, and a causal response time window established at the feeding trigger moment is combined for effectiveness identification, eliminating airflow artifacts caused by material collapse and solid waste deflagration. Finally, the effective pulse broadening value and skewness coefficient are incorporated into a unified exponential evaluation framework, allowing time-domain hysteresis and morphological distortion to complement each other. The entire method adapts to operating condition fluctuations without requiring manual setting of fixed amplitude thresholds, achieving stable identification and early warning of the flap valve's mechanical state even in harsh signal environments with strong low-frequency interference and airflow artifacts.

[0016] 2. This invention fuses the absolute value of the skewness coefficient with the broadening value using a nonlinear exponential penalty, making the two degradation modes of waveform asymmetric distortion and time-domain broadening complement each other and amplify exponentially. This effectively distinguishes between random process disturbances and substantial state degradation. At the same time, by combining the health state confidence threshold and multi-cycle delay judgment logic, it significantly reduces the false alarm rate while ensuring the timeliness of early warning, providing reliable support for the long-term safe operation of the cement preheater system. Attached Figure Description

[0017] Figure 1This is a flowchart of steps S1-S4 in a method for multi-source monitoring of cement production equipment status and early warning of faults according to an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0019] Reference Figure 1 A method for multi-source monitoring of cement production equipment status and early warning of faults includes steps S1-S4, as follows: S1: Synchronously collect the transient static pressure sequence of the upstream and downstream sidewalls of the preheater cyclone discharge pipe flap valve and calculate the difference point by point to obtain the original relative pressure difference sequence.

[0020] The preheater cyclone of cement production equipment and the flap valve installed on the cyclone discharge pipe operate under unsteady conditions of high temperature, negative pressure and material two-phase flow impact for a long time. The flap valve installed between the upper cyclone discharge pipe and the lower cyclone outlet heat exchange pipe inlet plays a key role in locking the air and ensuring uniform material discharge.

[0021] When the static pressure sensor installed on the upstream and downstream side walls of the flap valve collects absolute pressure signals, the macroscopic low-frequency baseline drift of the sensor output will be caused by the adjustment of the main exhaust fan speed, the distribution of system air volume and the fluctuation of fluid concentration. This low-frequency drift will submerge the high-frequency and weak pressure changes generated by the small opening and closing action of the flap valve, directly restricting the accuracy of condition monitoring.

[0022] To address the aforementioned issues, the system first performs a hardware clock synchronization initialization operation. This is achieved by using a network time protocol or a high-precision hardware trigger line to synchronize the hardware clocks of the upper-level and lower-level sidewall static pressure sensing nodes. This synchronization operation ensures strict alignment of the upper and lower level nodes on the time axis, directly eliminating phase deviations caused by sampling time differences. It should be noted that the network time protocol is a well-known technology and will not be elaborated upon here.

[0023] After clock synchronization, the system continuously acquires two sets of signals at the same sampling frequency: an upper transient static pressure sequence reflecting the upstream operating condition of the flap valve in the preheater cyclone discharge pipe, and a lower transient static pressure sequence reflecting the downstream operating condition. Furthermore, based on the fluid dynamics principle of perturbation equivalence in communicating vessels, the system performs point-by-point time-domain subtraction on these two sets of sequences to obtain the original relative pressure difference sequence, which characterizes the local fluid potential energy step between the upstream and downstream of the flap valve. This spatial physical subtraction operation physically isolates the macroscopic low-frequency common-mode baseline drift caused by the overall system ventilation, thus effectively preserving the high-frequency signal reflecting the true fluid potential energy step.

[0024] In addition, during data acquisition and differential calculation, the system is also equipped with sensor fault-tolerant logic to cope with signal interruptions under harsh operating conditions. When any signal from either the upper or lower sidewall static pressure sensing node is lost, the system will automatically freeze the update of the original relative pressure difference sequence and simultaneously report the abnormal probe status. The freeze will be lifted only after both sides of the signal return to normal, thereby ensuring the reliability of the output data and the safe operation of the system.

[0025] S2: Perform differential optimization on the original relative pressure difference sequence to lock the candidate pulse boundary, and based on the position of the candidate pulse boundary, cut back the local waveform corresponding to each candidate pulse in the original relative pressure difference sequence, and calculate the widening value and skewness coefficient of the local waveform.

[0026] The original relative pressure difference sequence obtained in S1 is mixed with high-frequency random white noise from the flow field. Furthermore, jamming or damping degradation of the flap valve's mechanical components can cause waveform broadening and asymmetric tailing distortion. Directly applying a fixed threshold to the original sequence is highly susceptible to noise interference and misjudgments. To address this issue, the system first performs a first-order time-domain differential operation on the original relative pressure difference sequence to obtain a differential sequence. The differential operation essentially extracts the time derivative information of the signal, acting as a high-pass filter that suppresses low-frequency baseline components and highlights high-frequency abrupt changes. Based on this, the system combines dynamic threshold logic based on the zero-crossing rate detection of the differential sequence. When the differential sequence changes from positive to negative or from negative to positive and crosses the zero line, it is determined as the start or end time of a pulse. This allows the system to locate the start and end boundary points of each pulse in the differential sequence, adaptively locking all possible candidate pulse boundaries corresponding to the flap valve's opening and closing actions. A complete candidate pulse is defined by a pair of start and end boundaries, and the time span between the start and end boundaries is the duration of the candidate pulse. Differential operations and zero-crossing rate detection are well-known technologies and will not be elaborated here.

[0027] After the start and end boundaries of the candidate pulse are locked, the system will backtrack to the waveform segment with the same time index in the original relative pressure difference sequence according to the time position of each candidate pulse enclosed by the start and end boundaries, and extract the segment as a local waveform.

[0028] In other words, the candidate pulse boundaries in the differential sequence are only used as positioning markers. The starting boundary marks the moment when the pulse begins to rise, and the ending boundary marks the moment when the pulse returns to the baseline level. These are used to determine the specific moment and duration of the potential action of the flap valve. The waveform data that actually participates in the feature calculation comes from the original relative pressure difference sequence in the same time interval without differential processing.

[0029] Through the above operations, the system transforms each candidate pulse—that is, the interval enclosed by each pair of start / end boundaries—into a corresponding local waveform segment in the original relative pressure difference sequence. The number of local waveform segments is equal to the number of boundary pairs, with each candidate pulse independently corresponding to a local waveform segment. This strategy is adopted because first-order differential operations, while amplifying abrupt signal, also amplify high-frequency noise and alter the physical shape of the original waveform. The waveform broadening value and skewness coefficient belong to the morphological characteristics of the amplitude domain and must be calculated on the original amplitude to ensure the accuracy of their physical meaning.

[0030] For the captured local waveform, the mean amplitude and standard deviation of the amplitude are first calculated to obtain the statistical distribution characteristics of the local waveform. To avoid numerical overflow under low-fluctuation conditions, a corrected standard deviation is introduced, which is expressed by the following formula:

[0031] In the formula, To correct for standard deviation, The standard deviation of the amplitude. Let be the minimum constant, and take the empirical value as . It can also be adjusted by the implementer according to the specific implementation scenario. The smaller the value, the weaker the impact on the correction of normal fluctuation amplitude. The larger the value, the more aggressive the overflow protection, but it may slightly compress the amplitude of the skewness coefficient.

[0032] After obtaining the local statistical parameters, the system extracts the time width corresponding to half of the amplitude peak in the local waveform as the local waveform broadening value. The physical meaning of this value is the time span between the rising edge half-high point and the falling edge half-high point of the transient pulse. Its magnitude is positively correlated with the time required for the flap valve to complete one full opening and closing action. An increase in the broadening value indicates that the valve action is slow or the damping is increased, reflecting the intensified mechanical hysteresis characteristics. A decrease in the broadening value indicates that the valve action is crisp and clean, reflecting good mechanical condition.

[0033] At the same time, the skewness coefficient of the local waveform is extracted, which can be expressed by the following formula:

[0034] This represents the skewness coefficient of the local waveform. To correct for standard deviation, For the local waveform, the first The amplitude of each sampling point The average amplitude of the local waveform. This represents the total number of sampling points contained in the local waveform.

[0035] When the waveform is symmetrical, meaning the rising and falling edges have the same shape, the positive and negative deviations cancel each other out. Approaching zero; when the waveform has a right tail (i.e., the falling edge is slower than the rising edge) and the pressure recovery is slow, the positive deviation dominates. The value is negative, and the smaller the negative value, the more pronounced the left tail. In the actual operation of the flap valve, when mechanical parts become stuck, the valve plate opening is obstructed, causing the pressure rise to slow down, i.e., a left tail, and the skewness coefficient tends to be negative; when damping deteriorates or the valve plate does not close tightly, the pressure relief process is prolonged, i.e., a right tail, and the skewness coefficient tends to be positive; under normal conditions, the waveform is basically symmetrical, and the skewness coefficient is close to zero. Therefore, the degree of deviation of the skewness coefficient can be directly used as a criterion for quantifying the mechanical degradation of the flap valve.

[0036] After performing the above calculations on the local waveform of each candidate pulse, the broadening value and skewness coefficient of the local waveform of all candidate pulses are output as two core characteristic data to characterize the transient hydrodynamic morphology of the flap valve: the broadening value reflects the degree of action hysteresis, and the skewness coefficient reflects the symmetry / stuckness of action. The two complement each other and provide multi-dimensional quantitative basis for subsequent condition monitoring and fault diagnosis.

[0037] S3: Set the expected response time window based on the feeding trigger time and the material falling and transport delay, confirm the candidate pulses falling into the response time window as valid pulses, and eliminate airflow artifacts outside the response time window.

[0038] Under the complex combustion conditions of cement production equipment, material collapse or solid waste deflagration can trigger strong thermodynamic airflow impacts, producing pseudo-pulse waveforms highly similar to the actual mechanical action of the flap valve. This results in these pseudo-pulse components being mixed into a series of candidate pulses output in step S2, causing homomorphic aliasing of the signals and directly triggering false alarms in the status monitoring. Since there is a definite physical causal feedforward relationship between the feeder's control cycle and the impact of material gravity on the flap valve—that is, each feeding action will inevitably and only can trigger a flap valve mechanical response once after a fixed time delay—and the airflow pseudo-pulses are unrelated to this physical law on the time axis, this causal constraint can be used to effectively distinguish the validity of all candidate pulses output by S2.

[0039] First, the trigger time of the feeder pulse control cycle issued by the external master control node is extracted and set as the feeding trigger time. This feeding trigger time serves as a rigid feedforward physical time anchor point, establishing the starting time reference for the material falling.

[0040] The material needs a fixed physical transmission time to fall from the feed port to the flap valve position. The system extracts the median of the historical material falling time difference during the initial fault-free operation phase of the equipment as the material falling transmission delay. The physical meaning of this material falling transmission delay is the average transmission time required for the material to be released from the flap valve under the action of gravity and airflow. It is the core time link connecting the feeding command and the mechanical response. The larger the value, the longer the material transmission path or the greater the airflow resistance. The smaller the value, the faster the material response.

[0041] Since transmission delay has random variance due to factors such as airflow speed fluctuations, material concentration changes, and system negative pressure drift in actual operation, the transmission delay standard deviation is statistically calibrated based on historical data of healthy operating conditions to calculate the delay tolerance.

[0042] The specific calculation process for the above delay tolerance is as follows: First, historical data of the equipment during the fault-free operation phase is extracted. For each feeding action, the time difference between the feeding trigger time and the actual response time of the flap valve is recorded as a transmission delay sample. Then, the standard deviation of all samples is calculated as the transmission delay standard deviation of the historical data under healthy operating conditions. This value reflects the inherent natural fluctuation range of transmission delay under normal equipment conditions.

[0043] Then, the standard deviation of transmission delay in the aforementioned historical health data is multiplied by a preset statistical confidence factor to obtain the delay tolerance. Physically, this tolerance represents the time margin reserved by the system to overcome random fluctuations in transmission delay, accommodating the earlier or later material drop time caused by airflow disturbances under normal operating conditions. A larger value results in a wider time window and a stronger ability to tolerate spurious pulses, but also a higher risk of mistakenly retaining them. A smaller value results in a narrower time window and a stronger ability to eliminate spurious pulses, but may mistakenly eliminate genuine, valid pulses that have shifted due to normal fluctuations. The statistical confidence factor is empirically set at 3. Its value determines the conservatism of the tolerance; a larger value results in a wider window and better robustness, but decreased selectivity, while a smaller value results in a narrower window and stronger selectivity, but decreased robustness. It can also be adjusted by the implementer according to the specific implementation scenario.

[0044] Furthermore, based on the feeding trigger time, material falling and transmission delay, and time delay tolerance, the system calculates the start and end times of the response time window respectively.

[0045] Since the physical transmission process exhibits symmetrical random fluctuations around the average delay under steady-state conditions, the time domain interval of the effective response is defined by adding or subtracting tolerance.

[0046] The start time of the above response time window can be expressed by the following formula:

[0047] In the formula, The earliest allowable time for a valid mechanical action response is defined as the start time of the response time window. Its physical meaning is the time obtained by subtracting the lower limit of the transmission delay after the feeding command is issued. Candidate pulses that appear earlier than this time cannot be physically caused by this feeding and must be pseudo pulses. For the feeding trigger time, Due to the delay in material falling and conveying, This refers to the latency tolerance.

[0048] The end time of the above response time window can be expressed by the following formula:

[0049] In the formula, The start time of the response time window defines the latest allowable time for an effective mechanical action response. Physically, this means the time obtained by adding the upper limit of the transmission delay after the feeding command is issued. Candidate pulses appearing later than this time also lose their causal relationship with the current feeding and are considered spurious pulses. For the feeding trigger time, Due to the delay in material falling and conveying, This refers to the latency tolerance.

[0050] along with As the window grows, the entire window moves synchronously to the right on the timeline; with... As the size increases, the window shifts to the right, reflecting a longer material transport path; with... As the window size increases, the window width expands accordingly, increasing inclusiveness but decreasing selectivity.

[0051] Since the impact of real material falling on the flap valve will inevitably produce one effective mechanical action within the response time window, meaning there is one and only one real pulse within the window corresponding to each feeding cycle, the system will identify the candidate pulses whose start and end boundaries fall within the response time window from all candidate pulses output by S2 as effective mechanical actions induced by the impact of real material falling; at the same time, the candidate pulses located outside the response time window, i.e., whose pulse start boundary is earlier than... Or the end boundary is later than The free candidate pulses are eliminated as airflow artifacts lacking physical causal support for the feeding action.

[0052] The physical function of this is to define the entry threshold of an effective pulse; candidate pulses whose starting boundary is earlier than this threshold are judged as leading noise. The physical function of the label is to define the exit boundary of the effective pulse. Candidate pulses whose end boundary is later than this boundary are judged as hysteresis noise. Together, they constitute a selective time-domain passband. Only candidate pulses whose start and end boundaries both fall completely within this passband are assigned a valid label.

[0053] After the system completes the above screening, it only outputs the local waveform data, its width value and skewness coefficient corresponding to the pulses that are confirmed as valid mechanical actions, for subsequent status monitoring and fault diagnosis, thereby fundamentally avoiding the interference of airflow artifacts on the status monitoring of the flap valve.

[0054] S4: The broadening value and skewness coefficient of the effective pulse are combined, and the state deviation evaluation index is calculated by nonlinear exponential penalty and compared with the safety boundary threshold to realize fault early warning.

[0055] The effective mechanical action pulses selected through the aforementioned steps reflect the mechanical hysteresis and damping degradation state of the flap valve from two dimensions: time domain width and morphological symmetry, respectively. However, single waveform characteristics exhibit random fluctuations under complex operating conditions, and direct judgment based on a single characteristic is susceptible to false alarms due to instantaneous process disturbances. Therefore, the system performs multi-dimensional feature fusion processing, transforming these two characteristics into a single equipment damping degradation index to improve the robustness of condition assessment.

[0056] First, the average value of the waveform width obtained statistically during the initial healthy operation cycle of the equipment is extracted and used as the reference width constant. This reference width constant represents the standard reference value of the pulse width of the equipment in a healthy state and serves as the reference zero point for subsequent deviation measurement.

[0057] Meanwhile, the system determines the damping sensitivity amplification index based on the static configuration of the tolerance to mechanical degradation. This damping sensitivity amplification index is a dimensionless parameter that determines the amplification of the evaluation index when the waveform broadening deviates from the reference value. The larger the value, the more severe the impact of the broadening deviation on the final index, and the higher the system's sensitivity to damping degradation detection, but it may also amplify normal fluctuations. The smaller the value, the smoother the change in the evaluation index, the stronger the anti-disturbance capability, but the less sensitive it is to early degradation.

[0058] After determining the aforementioned baseline parameters, the system performs a nonlinear exponential penalty calculation. Since the skewness coefficient may become negative during abnormal mechanical movements (corresponding to a left-tailed pattern where the rising edge is slower than the falling edge), directly using negative values ​​in the exponential calculation would lead to abnormal attenuation of the evaluation results or an inability to accurately reflect the degree of degradation. Therefore, the system takes the absolute value of the skewness coefficient to obtain a skewness absolute value constraint term, ensuring that regardless of the direction of asymmetric waveform distortion, it can be converted into a positive penalty contribution. Based on the skewness absolute value constraint term, waveform broadening value, baseline broadening preset constant, and damping-sensitive amplification index, the system calculates the state deviation evaluation index through a nonlinear exponential penalty model.

[0059] The above-mentioned deviation from the evaluation index is expressed by the following formula:

[0060] In the formula, The deviation assessment index is dimensionless and is used to characterize the overall damping degradation of the current mechanical action relative to the health benchmark. The larger the value, the further the mechanical state deviates from the health benchmark. This represents the skewness coefficient of the local waveform. This is the absolute value constraint term for skewness. When the waveform is symmetrical, this term approaches 1 and does not produce additional penalty. When the waveform is asymmetrically distorted due to abnormal mechanical conditions, this term grows exponentially, penalizing the abnormal shape. The more severe the distortion, the greater the penalty. This represents the widening value of the local waveform. As the baseline broadening constant, This is the damping sensitivity amplification factor. This is a widening penalty term. When the widening value equals the baseline value, this term is 1, resulting in no penalty. When the widening value exceeds the baseline value, this term is greater than 1, and the penalty increases with the degree of widening deviation. The exponential relationship amplifies the penalty, directly reflecting the degree of sluggishness in the action. The larger the limit, the more aggressive the punishment.

[0061] By using complementary weighting of the two features, the absolute value constraint term is sensitive to waveform morphology asymmetry, and the broadening penalty term is sensitive to time domain extension, so that the waveform distortion caused by mechanical damping degradation is exponentially monotonically amplified, thereby effectively distinguishing healthy fluctuations from substantial state degradation.

[0062] Because the equipment's health condition deviates from the assessment index It also has its own inherent statistical distribution patterns. Even in a healthy state, various characteristics will have slight random fluctuations. The system extracts all the data calculated within the initial healthy operating cycle of the equipment. Historical data were used to fit the probability density distribution function of the healthy state using kernel density estimation. Kernel density estimation is a well-known technique and will not be elaborated upon here.

[0063] Based on this probability distribution, the system extracts its 99% confidence upper limit as a dimensionless safety boundary threshold. This safety boundary threshold physically defines the boundary between normal equipment operation and degradation; in a healthy state... There is a 99% probability that it will fall below this safety boundary threshold. Exceeding this safety boundary threshold means that the current state has exceeded the range of healthy fluctuations, indicating possible mechanical degradation.

[0064] The system uses the feeder pulse as a monitoring cycle and performs a real-time sliding comparison between the real-time state deviation evaluation index calculated from the effective pulses in each feeding cycle and the safety boundary threshold.

[0065] When the real-time status deviates from the evaluation index and exceeds the safety boundary threshold in a single instance, the system internally marks the status, but does not immediately issue an alert. This delayed judgment logic is introduced because a single violation may be caused by random process fluctuations such as instantaneous changes in material concentration or airflow disturbances; if an alert were issued immediately, the false alarm rate would be too high.

[0066] Therefore, the system continuously monitors the evaluation results of subsequent feeding cycles and executes multi-cycle limit judgment logic. When the system determines that the state deviation evaluation index exceeds the safety boundary threshold for three consecutive feeding cycles, it confirms that the current state deviation is statistically significant and not caused by random process fluctuations, thus determining that the flap valve has shown a substantial state degradation trend. At this time, the system immediately generates an early isolation and engineering warning instruction carrying the equipment level code and precise timestamp, and pushes the instruction to the field maintenance terminal to notify the operation and maintenance personnel to intervene and check in a timely manner, thereby achieving early warning before the fault completely deteriorates.

[0067] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for multi-source monitoring of the condition and early warning of faults in cement production equipment, characterized in that, include: The transient static pressure sequence of the upstream side wall and the transient static pressure sequence of the downstream side wall of the preheater cyclone discharge pipe flap valve are collected simultaneously, and the difference between the two sequences is calculated point by point to obtain the original relative pressure difference sequence. Perform a first-order time-domain difference operation on the original relative pressure difference sequence to obtain a difference sequence, and lock the start and end boundaries of the candidate pulses based on the zero-crossing condition of the difference sequence; Based on the starting and ending boundaries, local waveforms corresponding to each candidate pulse are extracted from the original relative pressure difference sequence, and the broadening value and skewness coefficient of the local waveforms are calculated. A response time window is set based on the feeding trigger time and the material falling and transporting delay. Candidate pulses whose start and end boundaries both fall within the response time window are confirmed as valid pulses. By combining the broadening value and skewness coefficient of the effective pulse, a state deviation assessment index is calculated, and the state deviation assessment index is compared with a safety boundary threshold to achieve fault early warning; The state deviation evaluation index is calculated by fusing the broadening value and skewness coefficient of the effective pulse, including: The mean value of waveform broadening obtained statistically during the initial healthy operation cycle of the equipment is extracted as the reference broadening constant; the damping sensitive amplification index is preset; the absolute value of the skewness coefficient is taken; based on the absolute value of the skewness coefficient, the broadening value, the reference broadening constant and the damping sensitive amplification index, the state deviation evaluation index is calculated, and the state deviation evaluation index is compared with the safety boundary threshold to realize fault early warning; The calculation of the state deviation assessment index includes: In the formula, The deviation assessment index is dimensionless and is used to characterize the overall damping degradation of the current mechanical action relative to the health benchmark. The larger the value, the further the mechanical state deviates from the health benchmark. This represents the skewness coefficient of the local waveform. This is the absolute value constraint term for skewness. When the waveform is symmetrical, this term approaches 1 and does not produce additional penalty. When the waveform is asymmetrically distorted due to abnormal mechanical conditions, this term grows exponentially, penalizing the abnormal shape. The more severe the distortion, the greater the penalty. This represents the widening value of the local waveform. As the baseline broadening constant, This is the damping sensitivity amplification factor. This is a widening penalty term. When the widening value equals the baseline value, this term is 1, resulting in no penalty. When the widening value exceeds the baseline value, this term is greater than 1, and the penalty increases with the degree of widening deviation. The exponential relationship amplifies the penalty, directly reflecting the degree of sluggishness in the action. The larger the limit, the more aggressive the punishment.

2. The method for multi-source monitoring of cement production equipment status and early warning of faults according to claim 1, characterized in that, Before synchronously acquiring the transient static pressure sequence of the upstream sidewall and the downstream sidewall of the preheater cyclone discharge pipe flap valve, the process also includes: Perform a hardware clock synchronization operation to synchronize the clocks of the upstream sidewall static pressure sensing node and the downstream sidewall static pressure sensing node.

3. The method for multi-source monitoring of cement production equipment status and early warning of faults according to claim 1, characterized in that, The data collection and subtraction process also includes: When a signal is lost on one side, the update of the original relative pressure difference sequence is frozen and the abnormal status of the probe is reported. The freeze is lifted after the signals on both sides return to normal.

4. The method for multi-source monitoring of cement production equipment status and early warning of faults according to claim 1, characterized in that, The step of locking the start and end boundaries of candidate pulses based on the zero-crossing condition of the difference sequence includes: When the differential sequence crosses the zero line, the crossing position is determined as the start or end boundary of the candidate pulse.

5. The method for multi-source monitoring of cement production equipment status and early warning of faults according to claim 1, characterized in that, The calculation of the widening value of the local waveform includes: The time width corresponding to half of the amplitude peak in the local waveform is extracted and used as the widening value.

6. The method for multi-source monitoring of cement production equipment status and early warning of faults according to claim 1, characterized in that, The method of setting a response time window based on the feeding trigger time and the material falling and conveying delay includes: The feed trigger time is defined as the pulse control cycle trigger time of the feeder. The median of the historical material drop time difference during the initial fault-free operation phase of the equipment is taken as the material drop and transmission delay. The latency tolerance is calculated based on the standard deviation of transmission latency from historical health condition data. The start time of the response time window is equal to the feeding trigger time plus the material falling transmission delay minus the delay tolerance, and the end time of the response time window is equal to the feeding trigger time plus the material falling transmission delay plus the delay tolerance.

7. The method for multi-source monitoring of cement production equipment status and early warning of faults according to claim 6, characterized in that, The calculation of latency tolerance based on the standard deviation of transmission latency from historical health condition data includes: Record the time difference between the feeding trigger time and the actual response time of the flap valve for each feeding action during the fault-free operation phase of the equipment, as a transmission delay sample; Calculate the standard deviation of all transmission delay samples; The delay tolerance is obtained by multiplying the standard deviation of all transmission delay samples by a preset statistical confidence factor.

8. The method for multi-source monitoring of cement production equipment status and early warning of faults according to claim 1, characterized in that, The step of comparing the state deviation assessment index with a safety boundary threshold to achieve fault early warning includes: Extract all historical data of the state deviation evaluation index calculated during the initial healthy operation cycle of the equipment, and fit the probability density distribution function of the healthy state. Extract the 99% confidence upper limit of the probability density distribution function as the safety boundary threshold; The real-time state deviation evaluation index calculated in each feeding cycle is compared with the safety boundary threshold. When the real-time status deviates from the evaluation index and exceeds the safety boundary threshold for three consecutive feeding cycles, an early warning command is generated.

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