A method for optimizing the energy consumption of a variable frequency motor in a shot blasting machine

Abstract

This invention relates to the field of variable frequency motor control technology, and discloses a method for optimizing and adjusting the energy consumption of a variable frequency motor in a shot blasting machine. The method includes: acquiring the real-time current signal of the motor; converting its inherent high-frequency ripple shape into a discrete gear signal reflecting the mechanical load through a signal processing module; filtering the discrete gear signal using a confirmation time window to suppress instantaneous impacts; and then matching and determining the target operating frequency in a lookup table based on the effective gear signal for feedforward adjustment. This invention eliminates the reliance on external load sensors, basing the adjustment entirely on the electromagnetic characteristics of the motor itself. This not only structurally avoids the risk of failure due to sensor malfunctions, but also replaces the response delay of traditional feedback control with an instantaneous feedforward approach in the control logic, achieving close tracking of energy consumption and load.

Description

Technical Field

[0001] This invention relates to a method for optimizing and adjusting the energy consumption of a variable frequency motor in a shot blasting machine, belonging to the field of variable frequency motor control technology. Background Technology

[0002] Currently, in industrial equipment such as shot blasting machines, in order to adapt to changing processing loads and reduce energy consumption, inverters are usually used to control the drive motor. On this basis, in order to achieve adaptive load adjustment, a common technical approach is to add external sensors to the equipment, such as photoelectric switches or pressure sensors, to detect the presence or absence of workpieces or the magnitude of the load, and use the detection signals to trigger the inverter to switch between different operating frequencies.

[0003] However, the aforementioned technology relying on external sensors faces inherent limitations in practical industrial applications. The working environment of shot blasting machines is usually characterized by severe vibration and a large amount of dust, which poses a challenge to the long-term reliability of external sensors. The installation, wiring, calibration, and regular maintenance of sensors increase the overall cost of the equipment. Furthermore, if the sensors are damaged due to environmental factors, it will directly lead to the failure of the control system, thereby causing unplanned downtime and resulting in corresponding production losses.

[0004] To address this issue, some approaches attempt to predict load using more complex algorithmic models or employ higher-performance but more expensive sensor types. The former proves insufficiently adaptable due to the random variations in workpiece size, position, and density during production; the latter exacerbates the conflict between cost and reliability. These approaches reveal that the crux of the problem lies in the control architecture that separates sensing and execution, failing to utilize information directly related to load status from the motor's own operating parameters. Specifically, existing technologies suffer from the following shortcomings: 1. The operational reliability and lifecycle cost of the control system are constrained by additional external sensors, making it difficult to meet long-term stable operation requirements under harsh conditions; 2. There is a signal delay in sensing the mechanical load borne by the motor, making it impossible to real-time adjust for transient load changes caused by irregular workpiece variations. Therefore, the technical problem this invention aims to solve is how to avoid dependence on external sensors and instead directly extract features reflecting the mechanical load status from the motor's own current signal in real time. Based on this, a simplified hardware structure with fast-response and long-term adaptive energy-efficient adjustment method is constructed. Summary of the Invention

[0005] This invention provides an energy consumption optimization and adjustment method for a variable frequency motor of a shot blasting machine. Its main purpose is to solve the problems of low system reliability, high cost, and inability to respond to load transients in real time due to reliance on external sensors in existing technologies.

[0006] To achieve the above objectives, the present invention provides a method for optimizing and adjusting the energy consumption of a variable frequency motor in a shot blasting machine, comprising the following steps:

[0007] Step a: Acquire the current signal of one or more phases of the variable frequency motor in real time and input the current signal to the signal processing module;

[0008] Step b: The signal processing module processes the current signal to output an integrated voltage value reflecting the mechanical load state of the motor. The signal processing module is an analog circuit module. The specific steps of the signal processing module processing the current signal to output the integrated voltage value are as follows: the power frequency fundamental wave in the current signal is filtered out by a high-pass filter in the analog circuit module to obtain a high-frequency ripple signal; and the high-frequency ripple signal is integrated by a rectifier-integrator circuit in the analog circuit module to output an integrated voltage value. Furthermore, the signal processing module compares the integrated voltage value with a first voltage threshold and a second voltage threshold stored in the controller to generate a discrete gear signal.

[0009] Step c: The controller receives discrete gear signals and only confirms the latest state of the received discrete gear signals as valid discrete gear signals if the duration of the latest state exceeds a confirmation time window set in the controller.

[0010] Step d: Based on the valid discrete gear signal, the controller matches and determines a target operating frequency in the state-frequency lookup table stored in the controller;

[0011] In step e, the controller sends the target operating frequency to the frequency converter to adjust the operation of the variable frequency motor.

[0012] Preferably, the state-frequency lookup table contains three discrete gears corresponding to the no-load state, working load state, and high load state of the shot blasting machine, respectively; and the comparison logic followed in step b for generating discrete gear signals is as follows: when the integrated voltage value is lower than the first voltage threshold, a discrete gear signal corresponding to the no-load state is generated; when the integrated voltage value is higher than or equal to the first voltage threshold and lower than the second voltage threshold, a discrete gear signal corresponding to the working load state is generated; when the integrated voltage value is higher than or equal to the second voltage threshold, a discrete gear signal corresponding to the high load state is generated.

[0013] Preferably, the method further includes a decision benchmark self-calibration step, which includes: the controller calculating and recording the proportion of the cumulative running time of the motor at each discrete gear in the previous working cycle to the total running time, in units of working cycle, to form real-time working time distribution data; comparing the real-time working time distribution data with the benchmark working time distribution model representing the healthy operating status of the equipment stored in the controller to obtain the distribution deviation value; and when the distribution deviation value continuously exceeds the calibration trigger threshold stored in the controller, the controller performs a small step adjustment on at least one of the first voltage threshold and the second voltage threshold according to the direction and magnitude of the distribution deviation value.

[0014] Preferably, the step of performing a small-step adjustment on at least one of the first voltage threshold and the second voltage threshold is defined by the following formula: ,in, For the adjusted new voltage threshold, The original voltage threshold before adjustment. It is a proportional adjustment coefficient stored in the controller. This is an error term in the distribution deviation value determined by the change in the proportion of the unloaded state time.

[0015] Preferably, the method further includes a frequency mapping table dynamic optimization step, which includes: the controller statistically analyzes the state transition frequency between discrete gear signals within a time window in the background, and calculates a condition characteristic index representing the macroscopic operating condition type based on the state transition frequency; the controller stores multiple independent state-frequency lookup tables corresponding to different macroscopic operating condition types; and the controller selects and activates a lookup table that matches the current macroscopic operating condition type from the multiple independent lookup tables according to the condition characteristic index, so as to perform the matching in step d and determine a target operating frequency.

[0016] Preferably, the method further includes a deep standby and sleep step, which includes: starting an idle timer when the effective discrete gear signal is the discrete gear corresponding to the no-load state; when the idle timer's timing duration reaches a first duration threshold, the controller sends a deep standby frequency to the inverter, which is lower than the frequency set for the no-load state in the state-frequency lookup table; and when the idle timer's timing duration reaches a second duration threshold, the controller sends a command to put the inverter into a sleep state.

[0017] Preferably, the method further includes a grid voltage feedforward compensation step, which includes: the controller acquiring the DC bus voltage value inside the inverter in real time; and the controller dynamically adjusting the first voltage threshold and the second voltage threshold in real time before the comparison operation in step b is executed, based on the ratio between the acquired DC bus voltage value and the reference voltage value stored in the controller.

[0018] Preferably, the method further includes a blade wear pre-diagnosis step, which includes: inputting the real-time current signal obtained in step a into a narrowband bandpass filter in parallel, wherein the center frequency of the narrowband bandpass filter corresponds to the rated operating speed frequency of the shot blasting wheel in the shot blasting machine; monitoring the output signal amplitude of the narrowband bandpass filter; and when the output signal amplitude exceeds the wear alarm threshold stored in the controller, the controller generates a pre-diagnosis alarm signal.

[0019] Preferably, the method further includes an energy efficiency micro-circulation self-optimization step within the gear position. The self-optimization step includes: after the controller confirms that the effective discrete gear position signal is stable in the same gear position for a longer period of time, applying a small frequency disturbance based on the target operating frequency determined in step d; synchronously monitoring the changing trend of the integral voltage value in step b; and the controller fine-tuning the target operating frequency according to the changing trend of the integral voltage value so that the integral voltage value tends to decrease.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. By acquiring the motor current signal and integrating its high-frequency ripple shape, an integral voltage value that directly reflects the mechanical load state is generated. This transforms the problem of continuously changing load identification into a decision on discrete load state levels. This method eliminates the reliance on external load sensors, not only structurally reducing system failure points caused by sensor malfunctions or environmental interference, but also avoiding the inherent signal acquisition and transmission delays of external sensors. Furthermore, by directly matching the discrete level signals with the stored state-frequency lookup table, feedforward control from load state decision to frequency adjustment command output is achieved. This allows the adjustment of the motor operating frequency to closely follow the instantaneous changes in the load, avoiding the energy waste caused by the lag response in traditional feedback control methods when the load fluctuates.

[0022] 2. This invention establishes a multi-timescale endogenous stabilization adjustment mechanism. At the millisecond-level instantaneous scale, by setting a confirmation time window, discrete gear signal jitter caused by instantaneous impacts on the workpiece, which does not represent the actual operating condition changes, is filtered out, maintaining the stability of the motor output. At the second-level operating condition scale, the DC bus voltage of the frequency converter is monitored in real time to reflect grid fluctuations, and the voltage threshold used for load status judgment is dynamically adjusted accordingly, so that the accuracy of load identification is freed from dependence on the stability of the external power grid. At the hundred-hour-level long-term scale, by statistically analyzing the cumulative running time distribution of each load gear, the slow drift of electrical characteristics caused by motor aging is identified, and the judgment benchmark is compensated and fine-tuned, maintaining the effectiveness of the adjustment method throughout the entire life cycle of the equipment. These three mechanisms work together in different time dimensions, enabling the adjustment method to cope with instantaneous signal impacts, grid voltage changes, and equipment parameter drift respectively.

[0023] 3. The method of this invention possesses the ability to identify macroscopic operating conditions and optimize control strategies. The controller, by statistically analyzing the state transition frequencies between different discrete speed range signals in the background, can generate an operating condition characteristic index representing the characteristics of the current production batch. Based on this index, the controller can select and activate a set matching the current operating condition from multiple pre-stored state-frequency lookup tables corresponding to different operating condition types. This allows the adjustment method to not only adapt to microscopic load transients but also to macroscopically identify overall changes in batches, such as workpiece material or size, and invoke the corresponding optimal frequency mapping relationship, achieving adaptive optimization of the control strategy. Furthermore, when the equipment is running stably at a certain speed range, by applying a small frequency disturbance and monitoring the response of the integral voltage value, it can also perform micro-efficiency optimization near the selected frequency speed range. Cyclic optimization deepens the adjustment precision from discrete gear levels to quasi-continuous optimization within gear levels. Furthermore, while achieving energy consumption regulation, this invention also provides a method for pre-diagnosing equipment health status using the same signal source. By inputting the acquired real-time current signal in parallel into a narrow-band bandpass filter with a center frequency corresponding to the rated speed frequency of the shot blasting wheel, specific frequency harmonic components caused by the wear imbalance of the shot blasting wheel blades can be separated. By monitoring the signal amplitude of this harmonic component and generating a pre-diagnostic signal when it exceeds the alarm threshold, early warning of critical mechanical component failures is achieved. This dual-source information processing method expands the functional dimensions of the control system without adding additional sensing hardware, elevating simple energy consumption regulation to comprehensive management integrating energy-saving control and predictive maintenance. Attached Figure Description

[0024] Figure 1 This is a flowchart of an energy consumption optimization and adjustment method for a variable frequency motor of a shot blasting machine according to the present invention.

[0025] Figure 2 This is a schematic diagram of the system functional architecture and core data flow of the present invention;

[0026] Figure 3 This is a diagram illustrating the load adaptive state transition and multi-level energy-saving strategy of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in further detail below. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention.

[0028] This invention discloses an energy consumption optimization and adjustment method for a variable frequency motor in a shot blasting machine. The technical solution is based on the analysis and feedforward control of the motor's operating electrical signals. The method mainly consists of a real-time current signal acquisition and processing stage, a load state identification stage based on discretization decision, and a frequency adjustment execution stage. These stages work together to achieve close tracking of energy consumption and load changes without relying on external sensors. In shot blasting applications for cleaning workpieces with irregular sizes and gaps, existing technologies face problems such as signal delays due to reliance on external sensors and insufficient reliability in harsh environments. This method provides an adjustment approach that uses the motor itself as a load sensor to address this issue. The method execution begins with step a, which involves real-time acquisition of one or more phases of the variable frequency motor's current signal. Considering that the high-frequency ripple pattern in the motor's armature current is directly related to the dynamic information of the mechanical load it bears, but… The information is superimposed on the fundamental frequency wave. To effectively extract this feature, step b is configured to input the acquired current signal into a signal processing module. In one specific embodiment, this signal processing module is an analog circuit module composed of a high-pass filter and a rectifier-integrator circuit. The high-pass filter filters out the fundamental frequency wave component in the current signal, allowing only the high-frequency ripple signal that reflects the instantaneous changes in the load to pass through. Subsequently, the rectifier-integrator circuit integrates the energy of the high-frequency ripple signal and outputs an integrated voltage value within a preset integration time. The magnitude of this integrated voltage value directly reflects the energy or intensity of the current ripple within that time window, thereby transforming a dynamic signal form into a single quantifiable DC voltage parameter. Furthermore, to transform the continuously changing load state into discrete instructions that can be directly processed by the controller, this integrated voltage value will be used in a comparison logic and compared with a first voltage threshold stored in the controller. Second voltage threshold A comparison is made to generate a discrete gear signal. The decision logic is set as follows: when the integral voltage value is lower than the first voltage threshold, a discrete gear signal corresponding to the no-load state of the shot blasting machine is generated; when the integral voltage value is higher than or equal to the first voltage threshold and lower than the second voltage threshold, a discrete gear signal corresponding to the working load state is generated; when the integral voltage value is higher than or equal to the second voltage threshold, a discrete gear signal corresponding to the high load state is generated. These three discrete gear signals constitute the decision basis for subsequent frequency adjustment.

[0029] When handling irregularly shaped workpieces with holes or sudden flips, the load may change drastically in a short period of time. Directly applying the aforementioned discrete gear signals would cause frequent fluctuations in the inverter's output frequency. To avoid this unsteady response caused by instantaneous impacts, step c introduces a logic filtering mechanism. After receiving the discrete gear signal, the controller does not immediately adopt it but activates a confirmation time window. Only when the duration of the latest received state exceeds this confirmation time window is the latest state confirmed as a valid discrete gear signal. Any state signal with a duration shorter than this window will be ignored by the controller, and the system will maintain the previous state unchanged. This measure can improve the system's adaptability to instantaneous physical impacts and maintain the stability of the motor output without adding hardware. After obtaining a valid discrete gear signal, step d performs feedforward frequency matching and determination. Based on the valid discrete gear signal, the controller matches and determines a target operating frequency in a state-frequency lookup table pre-stored within it. This lookup table establishes the mapping relationship between different load states and operating frequencies; for example, the no-load state corresponds to a standby frequency. The workload status corresponds to a job frequency. High load conditions correspond to a frequency that can meet peak demand. Finally, in step e, the controller sends the target operating frequency to the frequency converter, which then adjusts the motor's operating state. This feedforward control, from load state determination to frequency adjustment command output, enables the adjustment of the motor's operating frequency to closely follow changes in the load.

[0030] To address electrical characteristic drift caused by motor aging or component wear, a self-calibration step based on a decision benchmark is introduced. The controller, using a time cycle as a unit, statistically analyzes the proportion of the motor's cumulative running time at each discrete gear position to the total running time within the previous cycle, generating real-time time distribution data. Subsequently, the controller compares this real-time time distribution data with a preset benchmark time distribution model characterizing the equipment's healthy operating state to obtain a distribution deviation value. When this distribution deviation value continuously exceeds a calibration trigger threshold, the controller will adjust at least one of a first voltage threshold and a second voltage threshold in small steps, based on the direction and magnitude of the distribution deviation value. The new threshold is calculated using the following formula: ,in, For the adjusted new voltage threshold, The original voltage threshold before adjustment. It is a proportional adjustment coefficient, and This refers to the error term in the distribution deviation value determined by the change in the proportion of idle state time. This mechanism utilizes the statistical characteristics of the system's long-term behavior to maintain the core decision benchmark. To cope with changes in work type brought about by different production batch switching, a dynamic optimization step of frequency mapping table is configured. The controller counts the state transition frequency between discrete gear signals within a preset time window in the background and calculates a working condition characteristic index representing the work type based on the state transition frequency. Correspondingly, the controller pre-stores multiple independent state-frequency lookup tables corresponding to different work types. Based on the real-time calculated working condition characteristic index, the controller selects and activates a lookup table that matches the current work type from these multiple independent lookup tables for subsequent target operating frequency matching. This allows the adjustment method to not only adapt to load transients but also identify overall changes in production batches and call the corresponding frequency mapping relationship. This working condition characteristic index... The generation and application of [the system] follow a defined internal calculation procedure, which begins with the controller performing [operations] under no-load conditions within a preset macroscopic time window. workload High load Number of effective state transitions between the three gears and cumulative duration of each tier Statistical analysis is conducted, and based on these statistics, a dimensionless index value that quantifies the current macroeconomic conditions is calculated using the following formula. , ,in, and These are preset weighting coefficients, and their sum is 1. They are used to adjust the impact of the proportion of high load time and the frequency of high load entry on the composition of the indicators, respectively. and These represent the total runtime and the total number of state transitions within that time window, respectively; the controller ultimately bases its decisions on... With two-level operating condition decision threshold and Based on the comparison results, the corresponding state-frequency lookup table is selected and activated, and and These two thresholds are generated during the offline calibration phase of the equipment by forcing the equipment to process batches of heavy and light standard workpieces with clear physical properties and calculating their respective effects. It is determined based on the benchmark value.

[0031] To achieve energy saving from discrete gear levels to optimization within gear levels, a self-optimizing step involving a micro-cycle within gear levels is included. When the controller confirms that the effective discrete gear signal has stabilized at the same gear level for more than a certain stable duration, the system determines that it has entered a quasi-steady-state condition. At this point, a small periodic frequency disturbance is applied based on the target operating frequency determined for the current gear level. Simultaneously, the trend of the integral voltage value in step b is monitored. Since a decrease in the integral voltage value indicates an improvement in operating energy efficiency under constant load, the controller fine-tunes the target operating frequency accordingly to reduce the integral voltage value, thereby finding a more energy-efficient operating point in the neighborhood of that frequency gear level. The realization of this micro-cycle within gear levels relies on a gradient tracking control law based on synchronous demodulation, specifically, at the target operating frequency determined for the current gear level... On top of that, add an amplitude value Angular frequency is tiny sinusoidal perturbation signal Simultaneously, the controller synchronously acquires the integrated voltage value output by the signal processing module. And through calculation Changes and disturbance signals The cross-correlation value within the same period may be Multiply by a demodulated signal with the same frequency as the disturbance. Then, a low-pass filter is applied to obtain an error signal characterizing the energy efficiency gradient direction. The controller ultimately relies on this error signal The polarity and magnitude of the fundamental frequency are controlled by a proportional-integral regulator. Execute one step size Fine-tuning, that is This process iterates continuously at a set period until an error signal is detected. The energy converges to near zero, stabilizing the motor's operating point at the local optimum energy efficiency point under that load.

[0032] To address prolonged no-load conditions caused by process interruptions or system shutdowns, a deep standby and sleep mode is implemented. When a valid discrete gear signal confirms a no-load state, the controller starts a no-load timer. If the timer duration reaches a first threshold, the controller determines that the system has entered a long-term standby state and sends a deep standby frequency lower than the frequency corresponding to the no-load gear in the state-frequency lookup table to the inverter. If the timer duration further reaches a second threshold, the controller determines that the system has entered a shutdown state and sends a command to put the inverter into sleep mode. Once any non-no-load signal is detected, the system will immediately resume normal regulation. To address grid voltage fluctuations caused by the start-up and shutdown of other equipment in the workshop, a grid voltage feedforward compensation step is adopted. The controller obtains the internal DC bus voltage value from the inverter in real time via a communication protocol. Before performing the operation of comparing the integrated voltage value with the threshold, the controller compares the obtained DC bus voltage value with the reference voltage value. The proportional relationship allows for dynamic adjustment of the first and second voltage thresholds, providing the core decision logic with an operating environment unaffected by power grid disturbances and improving the method's adaptability to real electrical environments. Furthermore, this invention provides a method for pre-diagnosing equipment health status. The real-time current signal acquired in step a is input in parallel to a narrowband bandpass filter. The center frequency of this filter is set to correspond to the rated operating speed frequency of a shot blasting wheel in the shot blasting machine. When this component becomes unbalanced due to wear, a harmonic component of a specific frequency is generated in the current. This component is separated by the narrowband bandpass filter. The controller monitors the output signal amplitude of the filter and generates a pre-diagnostic alarm signal when it exceeds a wear alarm threshold. This expands the system's functionality without adding additional sensing hardware.

[0033] Example 1: In a continuously operating heavy machinery processing workshop, a shot blasting machine using the aforementioned technical solution faces the challenge of continuously processing a batch of workpieces that mix large, thick-walled cast iron parts with small, irregularly shaped, thin-walled parts. The workshop's power grid experiences voltage fluctuations due to the simultaneous start-up and shutdown of multiple high-power devices. When a large device starts, the workshop's power grid voltage drops, causing a decrease in the DC bus voltage inside the shot blasting machine's frequency converter. At this point, the power grid voltage feedforward compensation step is triggered. The controller reads the reduced DC bus voltage value and, based on its ratio to the reference voltage value, immediately lowers the first voltage threshold used for load condition determination. Second voltage threshold This baseline adjustment action, prior to load judgment, provides a judgment environment unaffected by grid disturbances for subsequent current ripple analysis. This transforms the interpretation of current signals from a process dependent on grid stability to one adaptable to grid fluctuations. Subsequently, a large cast iron component enters the shot blasting chamber, increasing the motor load. The high-frequency ripple in its phase current exhibits high amplitude and high density, and the integrated voltage value output by the signal processing module correspondingly rises, exceeding the dynamically adjusted second voltage threshold. Based on this, the controller generates discrete gear signals corresponding to high load conditions and matches the high load frequency in the state-frequency lookup table. The inverter output frequency is immediately increased to this value; thereafter, when a gap appears between the cast iron part and a subsequent series of small, thin-walled parts, the integrated voltage value drops back to the first voltage threshold. The controller then switches the frequency to the no-load frequency. .

[0034] Subsequently, the series of small, thin-walled parts, due to their irregular shape, tumbled continuously and rapidly within the shot blasting chamber. This caused severe but disordered pulse-like fluctuations in the motor load within a short period, and the integral voltage value oscillated rapidly between high and low thresholds. At this point, the confirmation time window mechanism came into play. Since the duration of these discrete gear signals caused by instantaneous impacts did not exceed the preset confirmation time window, they were identified as invalid transient signals by the controller and ignored. The system did not perform frequent frequency switching but maintained the no-load frequency previously determined by the workpiece gap. The controller only smoothly switches the frequency to the workload frequency once the stable incoming workpiece flow causes the integrated voltage value to remain stably between the first and second voltage thresholds and exceeds the confirmation time window. This mechanism allows the system to respond quickly to real load changes through feedforward lookup while suppressing overreaction to invalid signals through time window filtering, thus balancing response speed and output stability. Furthermore, when the shot blasting machine stabilizes at its operating load frequency... After continuously processing a batch of uniformly sized workpieces for more than a preset stabilization time, the energy efficiency micro-circulation self-optimization step within the gear is activated, and the controller... A small frequency disturbance is applied to the base, and the operating frequency is fine-tuned according to the feedback change trend of the integral voltage value to make it approach the optimal operating point under the current working conditions. This mechanism works in conjunction with the aforementioned discrete gear rapid switching mechanism. The former is responsible for rapid coarse adjustment and positioning when the load state changes abruptly, while the latter performs fine optimization within the state platform. Together, they form an adjustment system with both wide dynamic range and high precision. Finally, the processing of this batch of mixed workpieces was completed without abnormal frequency fluctuations. The motor's operating frequency and load type maintained dynamic matching throughout the process, and its core decision threshold, after a long working period, can be compensated for by the decision benchmark self-calibration step based on the historical running time distribution.

[0035] Example 2: To objectively verify the energy consumption optimization effect of the technical solution of the present invention in dealing with dynamic load changes, an experimental platform based on a standard industrial shot blasting machine was built. This platform consists of a three-phase asynchronous motor and its matching frequency converter, a controller for simulating load sequences, and a high-precision power analyzer. The experiment aims to compare the performance differences between the technical solution of the present invention and existing general technologies. Therefore, a control group and an experimental group were established. The control group operated at a fixed frequency, that is, the frequency converter was constantly set at 50Hz to meet the maximum load requirements. The experimental group applied the energy consumption adjustment method of the present invention, with the decision threshold of its integral voltage value... and After pre-collection and analysis of the current ripple of the motor under known no-load and different load conditions, the confirmation time window was set to 100ms. This selection aims to balance the system's response to real load changes and its ability to suppress transient noise lasting less than 100ms. The test process adopted a standardized load cycle with a duration of 60s, which consisted of four stages: 0s to 15s no-load, 15s to 35s heavy load, 35s to 45s no-load, and 45s to 60s working load. This cycle was reproduced by precisely controlling the mechanical load applied to the motor through the controller. During the test, the power analyzer simultaneously recorded the motor operating frequency and instantaneous power under the two sets of tests, and calculated the total energy consumption after each cycle.

[0036] During the test run, the two groups exhibited significant performance differences. In the two 25-second no-load phases, the control group maintained an instantaneous power level of 2.1kW to 2.2kW, while the test group's instantaneous power decreased to 0.6kW. Upon entering the heavy-load phase, both groups achieved a power of 7.5kW to meet load requirements. However, in the subsequent working load phase, the control group's power was 5.5kW, while the test group adjusted its power to 4.8kW according to load matching. This sustained power difference under non-heavy-load conditions was ultimately reflected in the total cycle energy consumption: the control group recorded 0.073kW. / h, while the experimental group's was 0.045kW / h; the energy consumption reduction of the experimental group stemmed from the effective operation of its regulation mechanism. During the no-load phase, the controller correctly determined the load status as no-load by analyzing the low-energy ripple pattern in the current signal, and output the corresponding low-energy standby frequency according to the status-frequency lookup table, thereby reducing the power from the 2.2kW no-load level to 0.6kW; during the working load phase, the controller also adjusted the frequency to 40Hz, which matches the actual load, instead of the 50Hz of the control group, by identifying the current ripple, thus achieving further optimization of power.

[0037] Example 3: This example combines Figures 1 to 3 This document describes a method for optimizing the energy consumption of a variable frequency motor in a shot blasting machine. Figure 1 As shown, the process begins by using the real-time motor current signal as the initial input. This signal is fed in parallel to the blade wear pre-diagnosis module to analyze specific harmonics and output a warning signal. Simultaneously, it is primarily fed to the signal processing module, which extracts high-frequency ripple and integrates and quantizes it into a voltage value. Next, the process enters the load state decision stage, comparing the integrated voltage value with a dynamic threshold to generate a discrete gear signal. This decision threshold simultaneously receives real-time dynamic adjustments from the grid voltage feedforward compensation module based on bus voltage monitoring, and compensatory adjustments from the decision benchmark self-calibration module based on long-cycle working hour distribution statistics. The generated discrete gear signal undergoes a logic filtering stage, employing a confirmation time window to suppress impacts. After forming a valid gear signal, it enters the frequency feedforward adjustment step. In this step, the system looks up a table to match the valid gear to determine the target frequency. Finally, in the motor operation adjustment stage, the frequency converter executes the frequency adjustment command, thereby completing the closed-loop adjustment and optimization of motor operation.

[0038] like Figure 2As shown in the diagram, the real-time current signal generated by the variable frequency motor is split into two paths. The main path enters the 1.0 signal processing and quantization module, which outputs the integral voltage value to the 2.0 load state judgment module. This module generates a valid discrete gear signal based on the voltage threshold read from the data storage D2 judgment voltage threshold and transmits it to the 3.0 adjustment command generation module. The latter generates a target operating frequency command based on the frequency mapping relationship retrieved from the data storage D1 state frequency lookup table and feeds it back to the variable frequency motor. At the same time, the 2.0 load state judgment module also outputs the cumulative duration information of each gear to the 4.0 judgment benchmark self-calibration module. This module generates an updated voltage threshold based on this and writes it back to the data storage D2. The other real-time current signal is input to the 5.0 blade wear pre-diagnosis module, and the pre-diagnosis alarm signal generated by it is sent to the maintenance system. Together, they form a comprehensive control system that integrates operation adjustment, benchmark self-calibration, and state pre-diagnosis.

[0039] like Figure 3 As shown, the core logic revolves around the transitions between idle, working, and high-load states. When the integrated voltage value is below threshold one, the system enters or remains in the idle state. When the integrated voltage value is between threshold one and threshold two, the system enters the working state and sets the operating frequency. When the integrated voltage value is higher than or equal to threshold two, the system enters the high-load state and sets the high-load frequency. In particular, within the idle state, a progressive energy-saving mechanism is designed: the system first enters the normal standby mode, sets the standby frequency, and starts the idle timer. If the idle state continues and the idle time exceeds the first duration threshold, the system will switch to deep standby mode and set a lower deep standby frequency. If the idle time further exceeds the second duration threshold, the system will finally enter the system hibernation state and issue a hibernation command to achieve maximum energy saving.

[0040] Example 4: When deploying a control system using the technical solution of this invention on a brand-new shot blasting machine for the first time, in order to match the control logic with the electrical characteristics of the specific motor of the shot blasting machine and the expected processing load, a standardized offline parameter calibration procedure needs to be executed. This procedure is a state-frequency lookup table, and a self-calibration step for voltage thresholds and decision benchmarks for load state judgment, providing initial settings with engineering basis. The procedure first determines the three core frequency parameters in the state-frequency lookup table. The operator runs the motor under three controllable load conditions and uses an external power analyzer to monitor its energy consumption data. In the first step, the motor is run with the shot blasting chamber completely emptied, and the frequency converter frequency is gradually reduced. The correspondence between the frequency and the motor idling power is recorded to determine a standby frequency that can maintain the system standby while keeping the power consumption below the inflection point. The second step is to add the workpiece with the maximum rated load that the equipment can bear into the shot blasting chamber, and determine the high load frequency required to drive its stable operation. This value is taken as the rated operating frequency of the motor; the third step is to use a typical workpiece that the equipment usually processes as a standard load, and determine the working load frequency by fine-tuning the frequency and finding the point of lowest power consumption, while ensuring cleaning efficiency. These three frequency values, determined through actual measurement, are fixed in the controller's state-frequency lookup table.

[0041] After the lookup table is set, the procedure proceeds to determine the threshold for discrete range signals. and The calibration was performed by the operator using the aforementioned determined parameters. , and It operates stably under corresponding no-load, working load, and high-load conditions, continuously acquiring the integrated voltage value output by the signal processing module at each stage; by statistically analyzing the acquired integrated voltage value data, its distribution range and mean under different load conditions are obtained; to ensure the reliability of the decision, a first voltage threshold is set. The second voltage threshold is set between the upper boundary of the no-load state integral voltage value distribution range and the lower boundary of the working load state distribution range. The threshold is set between two distribution ranges: workload and high load. This calibration method, based on measured data distribution, transforms threshold setting from empirical estimation into a reproducible engineering process. Finally, the proportional adjustment coefficient in the decision criterion self-calibration step is adjusted. This coefficient is set to balance the adjustment speed of the self-calibration mechanism with the long-term stability of the system; an excessively high value will result in a negative impact. An excessively low value can cause the system to overreact to short-term fluctuations in the working hour distribution, potentially leading to oscillations in the decision benchmark. Conversely, an excessively low value can slow down the calibration process, failing to promptly compensate for characteristic drift caused by motor aging. Therefore... The value is set such that when the cumulative duration distribution of a certain level within a work cycle deviates abnormally for an extended period, the resulting single threshold adjustment should be controlled within a preset range. This applies when the idle time percentage... The threshold adjustment range caused by a sustained deviation of 10%. The value is limited to between 1% and 2%. After all the parameters mentioned above have been calibrated and set, the control system has completed the initial configuration for the specific equipment. Its internal parameters are all determined values ​​based on measured data and engineering constraints, thus providing a foundation for its stable and efficient operation in the production environment.

[0042] Example 5: During the system deployment phase, to enable the dynamic optimization step of the frequency mapping table to handle various differentiated operations, multiple independent state-frequency lookup tables need to be constructed and stored. Based on the standard lookup table, this procedure first selects a batch of representative heavy workpieces and, under this specific load condition, repeatedly performs the test process used to determine the working load frequency and high load frequency, thereby obtaining a set of operating frequencies specifically for heavy workpieces. and And this set of frequencies is compared with the general standby frequency. The first set of dedicated heavy-load operation spectrum lookup tables is combined to form a second set of dedicated lookup tables for light-load operation spectrum. Then, in the same way, a batch of typical light-load workpieces are tested and calibrated to generate a second set of dedicated lookup tables for light-load operation spectrum. These two sets of dedicated lookup tables and the standard lookup table are stored in the controller, so that the system can call the frequency mapping relationship that matches the characteristics of the current operation batch according to the working condition characteristics index in subsequent production.

[0043] The alarm threshold setting in the blade wear pre-diagnosis step is accomplished by performing a baseline signal acquisition process when the shot blasting wheel is in brand new or intact condition. In this process, the equipment is run stably under standard workload for a preset period of time, and the signal amplitude output by the narrowband bandpass filter is continuously recorded. Given that the vibration signal of a balanced rotating system is at a relatively low stable level, the peak value of a series of signal amplitudes recorded during this stage is defined as the vibration baseline value of the equipment in this healthy state. Finally, the wear alarm threshold is set to 3 to 5 times this vibration baseline value. Thus, any signal exceeding this threshold in subsequent operation is considered as abnormal vibration caused by increased component imbalance, thereby providing a criterion for pre-diagnosis alarm.

[0044] Example 6: During the hardware design and debugging phase of the control system, in order to match the signal processing module with a specific type of shot blasting machine motor, the core parameters of the high-pass filter and rectifier-integral circuit in the module need to be set to establish a signal response baseline that accurately reflects the load state of the motor. This procedure first uses a spectrum analyzer to analyze the phase current signals collected by the target motor under various loads to determine the main energy distribution frequency band of the high-frequency ripple caused by mechanical load fluctuations. If the analysis shows that this frequency band is above 200Hz, then the cutoff frequency of the high-pass filter is set to filter out the 50Hz power frequency fundamental wave and retain the load characteristic signal. It is set to 100Hz.

[0045] After determining the cutoff frequency of the filter, the time constant of the subsequent rectifier-integrator circuit is then adjusted. Calibration is performed to ensure that the response speed of the integral voltage value is coordinated with the control cycle of the system. To achieve this, a step load is applied to the motor, switching its load state from no-load to heavy-load, and the integral voltage value curve at the output of the integrator circuit after high-pass filtering and rectification is observed. The time constant is adjusted by modifying the resistance and capacitance values ​​of the integrator circuit. Adjustments are made until the time it takes for the output integral voltage value curve to rise to a steady state matches the system control cycle, and the voltage ripple amplitude after stabilization is suppressed within a preset low level. The values ​​of this set of resistors and capacitors are then determined as circuit parameters. Through this procedure, the hardware parameters of the signal processing module are determined, enabling it to provide a high-quality and pre-processed input signal for the subsequent discrete gear signal decision logic.

[0046] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing and adjusting the energy consumption of a variable frequency motor in a shot blasting machine, characterized in that, Includes the following steps: Step a: Acquire the current signal of one or more phases of the variable frequency motor in real time and input the current signal to the signal processing module; Step b: The signal processing module processes the current signal to output an integrated voltage value reflecting the mechanical load state of the motor. The signal processing module is an analog circuit module. The specific steps of the signal processing module processing the current signal to output the integrated voltage value are as follows: the power frequency fundamental wave in the current signal is filtered out by a high-pass filter in the analog circuit module to obtain a high-frequency ripple signal; and the high-frequency ripple signal is integrated by a rectifier-integrator circuit in the analog circuit module to output an integrated voltage value. Furthermore, the signal processing module compares the integrated voltage value with a first voltage threshold and a second voltage threshold stored in the controller to generate a discrete gear signal. Step c: The controller receives discrete gear signals and only confirms the latest state of the received discrete gear signals as valid discrete gear signals if the duration of the latest state exceeds a confirmation time window set in the controller. Step d: Based on the valid discrete gear signal, the controller matches and determines a target operating frequency in the state-frequency lookup table stored in the controller; In step e, the controller sends the target operating frequency to the frequency converter to adjust the operation of the variable frequency motor.

2. The energy consumption optimization and adjustment method for a shot blasting machine variable frequency motor according to claim 1, characterized in that, The state-frequency lookup table contains three discrete gears corresponding to the no-load, working load, and high-load states of the shot blasting machine, respectively. The comparison logic followed in step b for generating the discrete gear signals is as follows: when the integrated voltage value is lower than the first voltage threshold, a discrete gear signal corresponding to the no-load state is generated; when the integrated voltage value is higher than or equal to the first voltage threshold and lower than the second voltage threshold, a discrete gear signal corresponding to the working load state is generated; when the integrated voltage value is higher than or equal to the second voltage threshold, a discrete gear signal corresponding to the high-load state is generated.

3. The energy consumption optimization and adjustment method for a shot blasting machine variable frequency motor according to claim 1, characterized in that, The method also includes a decision benchmark self-calibration step, which includes: the controller calculating and recording the proportion of the cumulative running time of the motor at each discrete gear in the previous working cycle to the total running time, in units of working cycle, to form real-time working time distribution data; comparing the real-time working time distribution data with the benchmark working time distribution model representing the healthy operating status of the equipment stored in the controller to obtain the distribution deviation value; and when the distribution deviation value continuously exceeds the calibration trigger threshold stored in the controller, the controller performs a small step adjustment on at least one of the first voltage threshold and the second voltage threshold according to the direction and magnitude of the distribution deviation value.

4. The energy consumption optimization and adjustment method for a shot blasting machine variable frequency motor according to claim 3, characterized in that, The step of performing a small-step adjustment on at least one of the first voltage threshold and the second voltage threshold, wherein the calculation of the new threshold is defined by the following formula: ,in, For the adjusted new voltage threshold, The original voltage threshold before adjustment. It is a proportional adjustment coefficient stored in the controller. This is an error term in the distribution deviation value determined by the change in the proportion of the unloaded state time.

5. The energy consumption optimization and adjustment method for a shot blasting machine variable frequency motor according to claim 1, characterized in that, The method also includes a dynamic optimization step for the frequency mapping table. The dynamic optimization step includes: the controller statistically analyzes the state transition frequency between discrete gear signals within a time window in the background, and calculates a condition characteristic index representing the macroscopic operating condition type based on the state transition frequency; the controller stores multiple independent state-frequency lookup tables corresponding to different macroscopic operating condition types; and the controller selects and activates a lookup table that matches the current macroscopic operating condition type from the multiple independent lookup tables according to the operating condition characteristic index, so as to perform the matching in step d and determine a target operating frequency.

6. The energy consumption optimization and adjustment method for a shot blasting machine variable frequency motor according to claim 1, characterized in that, The method also includes deep standby and sleep steps, which include: starting an idle timer when the effective discrete gear signal is the discrete gear corresponding to the no-load state; when the idle timer's timing duration reaches a first duration threshold, the controller sends a deep standby frequency to the inverter, which is lower than the frequency set for the no-load state in the state-frequency lookup table; and when the idle timer's timing duration reaches a second duration threshold, the controller sends a command to put the inverter into sleep state.

7. The energy consumption optimization and adjustment method for a shot blasting machine variable frequency motor according to claim 1, characterized in that, The method also includes a grid voltage feedforward compensation step, which includes: the controller acquiring the DC bus voltage value inside the inverter in real time; and the controller dynamically adjusting the first voltage threshold and the second voltage threshold in real time before the comparison operation in step b is executed, based on the ratio between the acquired DC bus voltage value and the reference voltage value stored in the controller.

8. The energy consumption optimization and adjustment method for a shot blasting machine variable frequency motor according to claim 1, characterized in that, The method also includes a blade wear pre-diagnosis step, which includes: inputting the real-time current signal obtained in step a into a narrowband bandpass filter in parallel, wherein the center frequency of the narrowband bandpass filter corresponds to the rated operating speed frequency of the shot blasting wheel in the shot blasting machine; monitoring the output signal amplitude of the narrowband bandpass filter; and when the output signal amplitude exceeds the wear alarm threshold stored in the controller, the controller generates a pre-diagnosis alarm signal.

9. The energy consumption optimization and adjustment method for a shot blasting machine variable frequency motor according to claim 1, characterized in that, The method also includes an energy efficiency micro-circulation self-optimization step within the gear position. The self-optimization step includes: after the controller confirms that the effective discrete gear position signal is stable in the same gear position for a longer period of time, a small frequency disturbance is applied based on the target operating frequency determined in step d; and the trend of the integral voltage value in step b is monitored synchronously.

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