Reciprocating trolley fault detection method and system based on displacement and current fusion judgment

Abstract

The invention discloses a reciprocating trolley fault detection method and system based on displacement and current fusion judgment, and relates to the technical field of industrial automation control and equipment fault diagnosis. The system comprises a displacement detection module, a current detection module, a data storage module, a core processing module and an alarm output module. The method comprises the following steps: acquiring trolley displacement and motor current data in real time; periodically judging whether the trolley has effective displacement or not through a displacement hysteresis comparison algorithm; judging whether the motor is in a no-load state or not through a current state recognition algorithm; double-criterion fusion decision logic is adopted, and when the conditions that displacement is not changed and current is no-load are met at the same time, it is judged that a hidden fault occurs, and an alarm is triggered; and further finely distinguishing fault types through the fault diagnosis tree. The problem that a traditional control system cannot recognize specific non-fault automatic stop is solved, and accurate and automatic fault detection and diagnosis are achieved.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control and equipment fault diagnosis technology, specifically to a method and system for detecting latent faults in reciprocating trolleys used in industries such as chemical fiber and textiles. Background Technology

[0002] Reciprocating trolleys are key equipment in chemical fiber production lines. Driven by servo motors, they perform precise reciprocating motions in both the horizontal and vertical directions to pull and lay up the yarn bundles. The continuity and stability of their operation are crucial to product quality. In actual production, reciprocating trolleys often experience a troubling "hidden" downtime phenomenon: the trolley stops running, but the PLC (Programmable Logic Controller) in the control center does not receive any standard fault alarms (such as overload, overcurrent, overspeed, etc.) from the frequency converter or drive system. This makes troubleshooting extremely difficult, leading to prolonged downtime and reduced production efficiency.

[0003] Based on field experience, this type of "hidden" shutdown is mainly caused by three reasons: 1) The servo inverter experiences a momentary power loss due to internal component problems, but fails to send a fault signal to the PLC; 2) The bearing at the bottom of the trolley breaks due to fatigue or poor lubrication, resulting in a significant reduction in friction, causing the motor drive wheel to spin in place and unable to move the trolley; 3) The drive shaft breaks, and the motor also spins in an idle state. These faults cannot be detected by traditional fault alarm mechanisms.

[0004] Currently, solutions to such problems mainly rely on the experience of operators or maintenance engineers. When the trolley stops and there are no alarms, personnel must rush to the site to check the inverter status indicator lights one by one, manually push the trolley to feel the resistance, and check the transmission components. This method is inefficient, highly dependent on personnel experience, and often leads to maintenance delays due to the lack of timely diagnostic information.

[0005] Therefore, there is an urgent need in the field for a system and method that can automatically and in real time detect and diagnose such "hidden" faults in order to enable predictive maintenance and reduce unplanned downtime. Summary of the Invention

[0006] Purpose of the invention: To provide a method and system for detecting faults in reciprocating trolleys based on the fusion of displacement and current, so as to solve the problem that existing technologies cannot automatically identify non-fault self-stopping of trolleys caused by abnormal power failure of frequency converters, bearing breakage or shaft fracture.

[0007] Technical solution: To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a reciprocating trolley fault detection system based on the fusion judgment of displacement and current.

[0008] The system includes: The displacement detection module is used to acquire the lateral and longitudinal displacement data of the reciprocating trolley in real time. A current detection module is used to acquire the operating current data of at least one motor driving the reciprocating trolley in real time; The data storage module is used to store historical displacement data, no-load current setpoints, and fault records. The core processing module, which is communicatively connected to the displacement detection module, the current detection module, and the data storage module, includes: The displacement hysteresis comparison unit is used to periodically compare the current displacement with the historical displacement and output the displacement comparison result; A current status identification unit is used to compare the real-time current value with the no-load current set value and output the current status result. The dual-criteria fusion decision unit is used to receive the displacement comparison result and the current state result. When both meet the preset alarm conditions, a hidden fault alarm is triggered. The fault type diagnosis unit is used to distinguish the specific causes of the latent fault according to a predefined diagnosis logic tree after the alarm is triggered. An alarm output module, connected to the core processing module, is used to output alarm information and diagnostic results.

[0009] In a further embodiment, the displacement hysteresis comparison unit operates using a displacement hysteresis comparison algorithm, specifically including: Record the current lateral displacement value in each detection cycle T. and longitudinal displacement value ; Retrieve historical displacement values ​​recorded from the previous detection cycle from the data storage module. and ; Calculate the displacement difference:

[0010]

[0011] Will and respectively with displacement tolerance threshold Compare; like and If the displacement is unchanged, the result of the displacement comparison is "unchanged"; otherwise, it is "changed". Wherein, the detection cycle and tolerance threshold These are configurable parameters.

[0012] In a further embodiment, the no-load current setting value is dynamically updated through a no-load current self-learning algorithm, the self-learning algorithm including: After system initialization or maintenance, the control vehicle runs a complete cycle in an unloaded state; During this period, motor current data was continuously collected, and its statistical distribution was calculated; The no-load current setting value The high quantile value of the statistical distribution is defined as follows:

[0013] in The mean of the current samples. Standard deviation For safety factors, the value ranges from 2 to 3.

[0014] In a further embodiment, the alarm condition of the dual-criteria fusion decision unit is: the displacement comparison result is "no change" and the current state result is "no load".

[0015] In a further embodiment, the diagnostic logic tree executed by the fault type diagnostic unit includes: If an alarm is triggered, first check the inverter power status register; If the register indicates a power supply abnormality, the diagnosis is "abnormal power failure of the frequency converter"; If the register indicates that the power supply is normal, the current characteristics are further analyzed: if the current fluctuation amplitude is less than the preset fluctuation threshold, the cause is diagnosed as "drive shaft breakage"; if the current fluctuation amplitude is greater than or equal to the fluctuation threshold, the cause is diagnosed as "bearing failure leading to insufficient friction".

[0016] Secondly, the present invention provides a method for detecting faults in reciprocating trolleys based on the fusion of displacement and current judgments, characterized by comprising the following steps: S1: System initialization, loading parameters, including detection cycle. Displacement tolerance threshold No-load current setting value ; S2: Real-time acquisition of displacement data of the reciprocating trolley and current data of the drive motor; S3: Execute the displacement hysteresis comparison algorithm to determine the period. Has the displacement of the inner trolley changed? S4: Execute the current state recognition algorithm to determine whether the motor is currently in an unloaded state; S5: Execute dual-criteria fusion decision: If the trolley displacement does not change and the motor is in an unloaded state, then a latent fault is determined to have occurred, and S6 is executed; otherwise, return to S2 to continue monitoring. S6: Trigger an alarm and execute the fault type diagnostic logic tree to determine the specific cause of the fault; S7: Output alarm signal and diagnostic results.

[0017] In a further embodiment, the displacement hysteresis comparison algorithm in step S3 is specifically as follows: Record the current moment displacement value ; Retrieve Historical displacement value at time ; Calculate displacement difference ; Compare With displacement tolerance threshold ; like The displacement was determined to be unchanged. like The displacement was determined to have changed.

[0018] In a further embodiment, the current state identification algorithm in step S4 is specifically as follows: Real-time collected current value With no-load current setting value Compare; like The motor is determined to be in an unloaded state. like This indicates that the motor is under load.

[0019] In a further embodiment, the fault type diagnosis logic tree in step S6 includes: S61: Check the inverter power supply status; S62: If the power supply status is power failure or abnormal, the diagnosis reason is "abnormal power failure of the frequency converter"; S63: If the power supply is normal, calculate the standard deviation of the current data within the time window before the alarm is triggered. ; S64: Will With the preset fluctuation threshold Compare; S65: If If so, the diagnosis is "drive shaft breakage"; S66: If The diagnosis is "insufficient friction due to bearing failure". In a further embodiment, the method further includes a no-load current self-learning step, used to automatically update the no-load current setpoint after system startup or receiving a maintenance completion signal. The self-learning steps include: The control vehicle is running unloaded; Collect N current sample data ; Calculate the sample mean and standard deviation ; According to the formula:

[0020] Calculate the new no-load current setting value, where The preset safety factor; New Stored to the data storage module.

[0021] Compared with the prior art, the present invention has the following significant advantages: It has achieved automated detection of hidden faults: It has completely changed the outdated mode of relying on manual inspection and experience-based troubleshooting. The system can achieve 24 / 7 uninterrupted automatic monitoring and diagnosis, realizing a seamless connection from "fault" to "diagnosis".

[0022] High detection accuracy and strong anti-interference capability: The use of a dual-criteria fusion technology based on displacement and current, cross-validating from two independent physical dimensions, significantly reduces the false alarm rate. The introduced displacement tolerance threshold and dynamically self-learning no-load current value enable the system to adapt to complex on-site conditions and the slow aging of equipment, exhibiting excellent robustness.

[0023] The invention offers refined diagnostic capabilities to guide maintenance decisions: Going beyond simply "detecting faults," this invention further differentiates three different root causes of faults through a diagnostic logic tree. This provides maintenance personnel with clear repair directions (such as checking the power supply, replacing bearings, or repairing the drive shaft), avoiding blind disassembly and replacement, and significantly shortening the mean time to repair (MTTR).

[0024] With self-learning and self-adaptive capabilities: Through the no-load current self-learning algorithm, the system can automatically update key thresholds after equipment overhaul or changes in operating conditions, avoiding detection failures caused by parameter fixation and reducing subsequent maintenance and debugging costs.

[0025] Flexible deployment and easy implementation: The core of this solution is the algorithm and logic, which can be integrated into the existing PLC control system or edge computing device in the form of software. It has low requirements for hardware modification and controllable implementation costs, making it very suitable for upgrading and promoting on existing production lines. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the overall module composition and data flow of the system of the present invention.

[0027] Figure 2 This is the overall flowchart of the latent fault detection method of the present invention.

[0028] Figure 3 This is a detailed flowchart of the displacement hysteresis comparison algorithm of the present invention.

[0029] Figure 4 This is a schematic diagram of the fault type diagnosis logic tree of the present invention.

[0030] Figure 5 This is a flowchart of the no-load current self-learning algorithm. Detailed Implementation

[0031] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] A reciprocating trolley fault detection system based on displacement and current fusion judgment includes: Displacement detection module: It usually consists of a high-precision rotary encoder mounted on a servo motor, used to measure and provide feedback on the lateral and longitudinal absolute position or relative displacement of the trolley in real time.

[0033] Current detection module: It uses a Hall effect current sensor or the analog output interface of the frequency converter itself to obtain the three-phase current or current vector amplitude of the drive motor in real time.

[0034] Data storage module: This can be the controller's internal registers, external memory card, or embedded database, used to store key parameters, historical operating data, and event records.

[0035] Core Processing Module: This is the core processing unit of the system, which can run as software in a programmable logic controller (PLC), embedded industrial computer, or edge computing gateway. It comprises four logical units: Displacement hysteresis comparison unit: Executes the displacement hysteresis comparison algorithm. It operates at a fixed detection cycle, comparing the current displacement with the displacement of the previous cycle. Considering sensor noise and minor vibrations, a displacement tolerance threshold is introduced. Only when the displacement change is less than or equal to the tolerance threshold is the trolley considered to have not undergone effective displacement.

[0036] Current Status Identification Unit: Executes the current status identification algorithm. It compares the real-time current with the no-load current setting value read from the data storage module. If the real-time current is less than or equal to the no-load current setting value, the motor is determined to be in a no-load state.

[0037] Dual-criteria fusion decision unit: Receives the outputs of the two units mentioned above. Only when both signals of unchanged displacement and no-load current are received simultaneously is a latent fault requiring an alarm determined to have occurred. This logic effectively filters out interference such as instantaneous resistance encountered during normal pauses and normal operation.

[0038] Fault Type Diagnosis Unit: This unit starts after an alarm is triggered and executes a diagnostic logic tree. First, it queries the inverter's status word or power monitoring point to determine if the problem is with the power supply. If the power supply is normal, it further analyzes the fluctuation characteristics of the short-term current window before the alarm. A stable no-load current usually indicates shaft breakage, while a fluctuating no-load current is more consistent with the characteristics of intermittent slippage caused by bearing breakage.

[0039] Alarm output module: Outputs the final alarm information and diagnostic results in various ways, such as lighting up the warning lights on the human-machine interface, sending information containing the specific diagnostic reasons to the maintenance personnel's mobile phone, or recording the event in the host computer management system.

[0040] A fault detection method for reciprocating trolleys based on the fusion of displacement and current judgment includes: System initialization, loading configuration parameters; Real-time synchronous acquisition of displacement and current data; Execute the displacement hysteresis comparison algorithm to determine whether the trolley has stopped; Execute the current state recognition algorithm to determine whether the motor is running idle; A dual-criteria fusion decision is made; if both conditions are met, an alarm is triggered. Execute the fault type diagnosis logic tree to accurately locate the cause of the fault; Output a diagnostic report.

[0041] Example 1: System Module Example See Figure 1 The core processing module of this system can be integrated into the programmable logic controller (PLC) of the production line. The displacement detection module sends the signal to the controller via a high-speed counter module. The current detection module sends the current value to the controller via an analog input module or fieldbus. The data storage module utilizes the controller's data blocks. The core processing module's program executes cyclically, and its output is connected to the alarm output module, which can control the human-machine interface display and send information to the cloud or mobile terminal via an industrial gateway.

[0042] Example 2: Method Flow Example See Figure 2 The method and process are as follows: After the system is powered on, it first performs initialization, loading parameters such as detection cycle, displacement tolerance threshold, and no-load current setting.

[0043] Enter the main loop and collect data in real time.

[0044] At each detection cycle, the displacement hysteresis comparison algorithm is invoked. For example... Figure 3 As shown, the algorithm calculates the displacement difference and compares it with the tolerance threshold, then outputs a displacement status flag.

[0045] Meanwhile, the current status recognition algorithm is continuously called in the main loop to compare the real-time current with the no-load current set value and output the current status flag.

[0046] At the decision point, a logical operation is performed on the two flag bits. If both conditions are met, a latent fault is determined to have occurred, and fault diagnosis is initiated.

[0047] like Figure 4 As shown, the inverter status word is first read through the controller. If the status word indicates a loss of main power, the diagnosis is an abnormal power failure of the inverter. If the power supply is normal, the current data from the previous time window is extracted, and its standard deviation is calculated. If the standard deviation is less than the fluctuation threshold, the current is considered stable, and the diagnosis is a broken drive shaft; otherwise, the diagnosis is insufficient friction due to bearing failure.

[0048] Finally, the diagnostic results will be displayed as a warning via the human-machine interface and sent to the maintenance supervisor's mobile phone.

[0049] Example 3: Self-learning algorithm example See Figure 5 After the bearings or drive shafts of the equipment are replaced, the technicians click the no-load self-learning button on the human-machine interface.

[0050] The system initiates a self-learning process, and the console vehicle runs a complete cycle without load.

[0051] During this period, the motor current is collected at a certain frequency, and a preset number of sample points are collected.

[0052] The algorithm calculates the mean and standard deviation of these sample points.

[0053] Take a safety factor and calculate the new no-load current setting value.

[0054] The new no-load current setting value is written to the data storage module, replacing the old value, thus completing the self-learning process.

[0055] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A reciprocating trolley fault detection system based on displacement and current fusion judgment, characterized in that, include: The displacement detection module is used to acquire the lateral and longitudinal displacement data of the reciprocating trolley in real time. A current detection module is used to acquire the operating current data of at least one motor driving the reciprocating trolley in real time; The data storage module is used to store historical displacement data, no-load current setpoints, and fault records. The core processing module, which is communicatively connected to the displacement detection module, the current detection module, and the data storage module, includes: The displacement hysteresis comparison unit is used to periodically compare the current displacement with the historical displacement and output the displacement comparison result; A current status identification unit is used to compare the real-time current value with the no-load current set value and output the current status result. The dual-criteria fusion decision unit is used to receive the displacement comparison result and the current state result. When both meet the preset alarm conditions, a hidden fault alarm is triggered. The fault type diagnosis unit is used to distinguish the specific causes of the latent fault according to a predefined diagnosis logic tree after the alarm is triggered. An alarm output module, connected to the core processing module, is used to output alarm information and diagnostic results.

2. The reciprocating trolley fault detection system based on displacement and current fusion judgment according to claim 1, characterized in that, The operation method of the displacement hysteresis comparison unit is a displacement hysteresis comparison algorithm, specifically including: Record the current lateral displacement value in each detection cycle T. and longitudinal displacement value ; Retrieve historical displacement values ​​recorded from the previous detection cycle from the data storage module. and ; Calculate the displacement difference: ； ； Will and respectively with displacement tolerance threshold Compare; like and If the displacement is unchanged, the result of the displacement comparison is "unchanged"; otherwise, it is "changed". Wherein, the detection cycle and tolerance threshold These are configurable parameters.

3. The reciprocating trolley fault detection system based on displacement and current fusion judgment according to claim 2, characterized in that, The no-load current setpoint is dynamically updated through a no-load current self-learning algorithm, which includes: After system initialization or maintenance, the control vehicle runs a complete cycle in an unloaded state; During this period, motor current data was continuously collected, and its statistical distribution was calculated; The no-load current setting value The high quantile value of the statistical distribution is set as follows: ； in The mean of the current samples. Standard deviation, For safety factors, the value ranges from 2 to 3.

4. The reciprocating trolley fault detection system based on displacement and current fusion judgment according to claim 1, characterized in that, The alarm conditions of the dual-criteria fusion decision unit are: the displacement comparison result is "no change" and the current state result is "no load".

5. The reciprocating trolley fault detection system based on displacement and current fusion judgment according to claim 1, characterized in that, The diagnostic logic tree executed by the fault type diagnostic unit includes: If an alarm is triggered, first check the inverter power status register; If the register indicates a power supply abnormality, the diagnosis is "abnormal power failure of the frequency converter"; If the register indicates that the power supply is normal, the current characteristics are further analyzed: if the current fluctuation amplitude is less than the preset fluctuation threshold, the cause is diagnosed as "drive shaft breakage"; if the current fluctuation amplitude is greater than or equal to the fluctuation threshold, the cause is diagnosed as "bearing failure leading to insufficient friction".

6. A fault detection method for reciprocating trolleys based on the fusion of displacement and current judgment, characterized in that, Includes the following steps: S1: System initialization, loading parameters, including detection cycle. Displacement tolerance threshold No-load current setting value ; S2: Real-time acquisition of displacement data of the reciprocating trolley and current data of the drive motor; S3: Execute the displacement hysteresis comparison algorithm to determine the period. Has the displacement of the inner trolley changed? S4: Execute the current state recognition algorithm to determine whether the motor is currently in an unloaded state; S5: Execute dual-criteria fusion decision: If the trolley displacement does not change and the motor is in an unloaded state, then a latent fault is determined to have occurred, and S6 is executed; otherwise, return to S2 to continue monitoring. S6: Trigger an alarm and execute the fault type diagnostic logic tree to determine the specific cause of the fault; S7: Output alarm signal and diagnostic results.

7. The method for fault detection of a reciprocating trolley based on the fusion judgment of displacement and current as described in claim 6, characterized in that, The displacement hysteresis comparison algorithm in step S3 is as follows: Record the current moment displacement value ; Retrieve Historical displacement value at time ; Calculate displacement difference ; Compare With displacement tolerance threshold ; like The displacement was determined to be unchanged. like The displacement was determined to have changed.

8. The method for fault detection of a reciprocating trolley based on the fusion judgment of displacement and current as described in claim 6, characterized in that, The current state identification algorithm in step S4 is as follows: Real-time collected current value With no-load current setting value Compare; like The motor is determined to be in an unloaded state. like This indicates that the motor is under load.

9. A method for fault detection of a reciprocating trolley based on displacement and current fusion judgment as described in claim 6, characterized in that, The fault type diagnosis logic tree in step S6 includes: S61: Check the inverter power supply status; S62: If the power supply status is power failure or abnormal, the diagnosis reason is "abnormal power failure of the frequency converter"; S63: If the power supply is normal, calculate the standard deviation of the current data within the time window before the alarm is triggered. ; S64: Will With the preset fluctuation threshold Compare; S65: If If so, the diagnosis is "drive shaft breakage"; S66: If The diagnosis is "insufficient friction due to bearing failure".

10. A method for detecting faults in a reciprocating trolley based on the fusion of displacement and current as described in claim 6, characterized in that, The method also includes a no-load current self-learning step, used to automatically update the no-load current setpoint after system startup or receiving a maintenance completion signal. The self-learning steps include: The control vehicle is running unloaded; Collect N current sample data ; Calculate the sample mean and standard deviation ; According to the formula: ; Calculate the new no-load current setting value, where The preset safety factor; New Stored to the data storage module.

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