Approach sensor failure determination method, apparatus, and landing gear retraction and extension control system

Abstract

The application relates to the technical field of proximity sensor position detection, and discloses a proximity sensor fault judgment method and device and a landing gear retracting and deploying control system. The method collects 2N sampling points under the response of a square wave falling edge under open circuit and short circuit reference states, collects N sampling points under the response of a square wave rising edge under a normal connection state, and extracts the position of a first wave crest; according to the position relationship between a real-time wave crest and a reference wave crest, N sampling points are dynamically extracted from the 2N reference sampling points for time domain phase alignment; the normalized mean square error of the aligned real-time wave and the open circuit and short circuit reference wave is calculated, and the normalized mean square error is compared with a preset normal interval to determine the sensor fault state. The application eliminates the influence of hardware trigger delay through wave crest anchor point alignment, quantifies wave form distortion in combination with the normalized mean square error, effectively overcomes the misjudgment problem caused by long cable distribution parameter interference, and realizes high-precision and low-power parallel fault detection without complex frequency domain transformation.

Description

Technical Field

[0001] This application relates to the field of proximity sensor position detection technology, and in particular to a proximity sensor fault diagnosis method, device, and landing gear retraction and extension control system. Background Technology

[0002] As a core component of the landing gear retraction and extension control system, the inductive proximity sensor is used to detect the "retracted" and "extracted" locking states of the landing gear and doors. Its principle is that the change in distance between the sensor and the target object causes a change in the inductance value of the coil inside the sensor. The inductance and resistance values ​​of the sensor are obtained through back-end AD sampling and DFT processing by FPGA, thereby determining the sensor's approach / distance normal state or open circuit / short circuit fault state.

[0003] When a sensor malfunctions, timely and accurate detection of the fault information is crucial. If a sensor malfunctions but is not detected, and the flight control system continues to control the landing gear and door retraction based on the sensor's state before the malfunction, it can lead to catastrophic consequences. Currently, sensor fault identification mainly relies on detecting resistance values, such as the method disclosed in Chinese Patent Publication No. CN115752520B. This method uses an FPGA to perform DFT calculations on the sampling points, and then calculates the resistance value using relevant formulas and interpolation filtering. The fault state is then determined based on the resistance value: if the resistance value Rs is less than 10Ω but greater than 0.1Ω, the sensor is set to a short-circuit fault state; if the resistance value Rs is greater than 50Ω or less than 0.1Ω, the sensor is set to an open-circuit fault state. This method is theoretically feasible, but in actual engineering projects, it has been found that the resistance values ​​obtained by measuring faulty sensors using this method are unstable, leading to a phenomenon where the fault state jumps back to the normal state, seriously affecting the landing gear and door retraction control.

[0004] The basic principle diagram for measuring the inductance and resistance of a sensor is shown below. Figure 1 As shown, the AC excitation source is a sine wave formed by shaping a standard square wave through a circuit. Within two adjacent cycles, N points are collected at positions 1 and 2 using an AD converter, and then subjected to Discrete Fourier Transform (DFT) processing to obtain the voltage vectors at positions 1 and 2, respectively. and The inductance and resistance values ​​of the sensor can be obtained using a known reference resistor R. ,in Let be a complex vector, and Re be the operation of taking the real part. However, in actual engineering projects, sensor cables can be tens of meters long, which is a factor that cannot be ignored; when the sensor malfunctions, electromagnetic interference and common-mode interference on the cable will directly affect the voltage vector. The measurement results in the calculated sensor inductance resistance value Rs being both positive and negative. Combined with interpolation filtering, the final resistance value may fall outside the fault state judgment range, causing the fault state to jump to the normal state and resulting in abnormal state judgment. Summary of the Invention

[0005] The purpose of this application is to provide a method, device and landing gear retraction and extension control system for judging proximity sensor faults, so as to solve the technical problem that the status judgment is abnormal due to the jump in the resistance value of the sensor in the case of fault.

[0006] To achieve the aforementioned objectives, the technical solution adopted in this application is as follows: In a first aspect, this application provides a method for determining a proximity sensor fault, comprising the following steps: Acquire the first calibration sampling sequence under the open circuit state and the second calibration sampling sequence under the short circuit state, and acquire the operating sampling sequence under the normal sensor connection state; The peak positions of the first calibration sampling sequence, the second calibration sampling sequence, and the running sampling sequence are determined respectively; Based on the peak position, target sampling point sequences aligned with the phase of the running sampling sequence are extracted from the first calibration sampling sequence and the second calibration sampling sequence, respectively; Calculate the normalized mean square error between the running sampling sequence and the two target sampling point sequences; Based on the relationship between the normalized mean square error and the preset normal range, it is determined whether the sensor has an open circuit fault or a short circuit fault.

[0007] In one possible implementation, a first calibration sampling sequence under open-circuit conditions and a second calibration sampling sequence under short-circuit conditions are acquired, along with an operational sampling sequence under normal sensor connection conditions, including: Triggered by the falling edge of the square wave excitation signal, 2N sampling points in the open-circuit state of the acquisition circuit are collected to generate the first calibration sampling sequence; and triggered by the falling edge of the square wave excitation signal, 2N sampling points in the short-circuit state of the acquisition circuit are collected to generate the second calibration sampling sequence. Triggered by the rising edge of the square wave excitation signal, N sampling points are collected under the normal connection state of the sensor to generate the running sampling sequence; where N is a positive integer.

[0008] In one possible implementation, calculating the normalized mean square error between the running sampling sequence and the two target sampling point sequences includes: The difference between each sampling point in the target sampling point sequence and the corresponding sampling point in the running sampling sequence is calculated, the squares are obtained, and the sums are accumulated to obtain the sum of squared errors. The squares of each sampling point in the target sampling point sequence are summed to obtain the baseline sum of squares. The ratio of the sum of squared errors to the sum of squared references is taken as the normalized mean square error.

[0009] In one possible implementation, determining whether the sensor has an open-circuit fault or a short-circuit fault based on the relationship between the normalized mean square error and a preset normal range includes: When the normalized mean square error of the running sampling sequence relative to the first calibrated sampling sequence is less than the lower limit of the preset normal range, it is determined that the sensor has an open circuit fault. When the normalized mean square error of the running sampling sequence relative to the second calibrated sampling sequence is greater than the upper limit of the preset normal range, the sensor is determined to have a short circuit fault.

[0010] In one possible implementation, based on the peak position, a sequence of target sampling points aligned with the phase of the running sampling sequence is extracted from the first calibration sampling sequence and the second calibration sampling sequence, respectively, including: The location of the first peak in the running sampling sequence is determined as the first reference position; The positions of the first peaks in the first calibration sampling sequence and the second calibration sampling sequence are respectively determined as the second reference position and the third reference position; Based on the offset relationship between the first reference position, the second reference position, and the third reference position, in the first calibration sampling sequence and the second calibration sampling sequence, offset the first reference position by a number of sampling points in the opposite direction of time increment, as the starting point for truncation; N sampling points are continuously extracted from the starting point of the extraction, which are used as the target sampling point sequence that is phase-aligned with the running sampling sequence; where N is a positive integer.

[0011] In one possible implementation, based on the offset relationship between the first reference position and the second and third reference positions, a number of sampling points are offset from the corresponding reference position in the first calibration sampling sequence and the second calibration sampling sequence in the opposite direction of time increment, serving as the truncation starting point, including: For the first calibrated sampling sequence, the index value of the second reference position is subtracted from the index value of the first reference position to obtain the first truncation starting point; For the second calibration sampling sequence, the index value of the third reference position is subtracted from the index value of the first reference position to obtain the second truncation starting point.

[0012] In one possible implementation, the rules for choosing the value of N include: N takes the value of an integer power of 2, and the minimum value of N is 64.

[0013] In one possible implementation, the method for constructing the preset normal interval includes: Under the condition that the sensor is properly connected and not malfunctioning, multiple sets of operational sampling sequences are collected; The multiple sets of running sampling sequences are combined in pairs, and the normalized mean square error corresponding to each pair of combinations is calculated. Extract the minimum and maximum values ​​of the normalized mean square error corresponding to all combinations, and construct the preset normal interval.

[0014] In one possible implementation, the multiple sets of running sampling sequences are combined pairwise, and the normalized mean square error corresponding to each pair of combinations is calculated, including: For any two sets of running sampling sequences, one set of running sampling sequences is used as the reference sequence, and the other set of running sampling sequences is used as the comparison sequence; The normalized mean square error of the alignment sequence relative to the reference sequence is calculated as the normalized mean square error corresponding to the two combinations.

[0015] In one possible implementation, determining the peak positions of the first calibration sampling sequence, the second calibration sampling sequence, and the running sampling sequence, respectively, includes: The first calibration sampling sequence, the second calibration sampling sequence, and the running sampling sequence are traversed respectively to find the maximum value of the voltage amplitude; The index position corresponding to the maximum point is determined as the peak position of the corresponding sampling sequence.

[0016] Secondly, this application provides a proximity sensor fault determination device, which applies the proximity sensor fault determination method described above, including: The sequence acquisition module is used to acquire the first calibration sampling sequence under the open circuit state and the second calibration sampling sequence under the short circuit state, and to acquire the operating sampling sequence under the normal connection state of the sensor. The peak determination module is used to determine the peak positions of the first calibration sampling sequence, the second calibration sampling sequence, and the running sampling sequence, respectively. The alignment and extraction module is used to extract target sampling point sequences that are phase-aligned with the running sampling sequence from the first calibration sampling sequence and the second calibration sampling sequence, respectively, based on the peak position. An error calculation module is used to calculate the normalized mean square error between the running sampling sequence and the two target sampling point sequences; The fault diagnosis module is used to determine whether the sensor has an open circuit fault or a short circuit fault based on the relationship between the normalized mean square error and the preset normal range.

[0017] Thirdly, this application also provides an electronic device, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the proximity sensor fault determination method described above.

[0018] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the proximity sensor fault judgment method as described above.

[0019] Fifthly, this application also provides a landing gear retraction and extension control system, including: Proximity sensors are used to detect the locking status of landing gear mechanical components; A controller, connected to the proximity sensor, is configured to execute the proximity sensor fault determination method described above to output an open-circuit fault or short-circuit fault status signal for the proximity sensor.

[0020] This application brings the following beneficial effects: This application automatically adjusts the phase and number of sampling points when a known fault occurs to the phase and number of sampling points when the sensor is normally connected, ensuring that the phase and number of the sampling points to be compared are completely consistent. This allows for normalized mean square error analysis of the sampling points, and through interval comparison, the sensor fault state can be directly determined. Furthermore, the sampling points to be analyzed are entirely derived from the sampling points during inductance measurement, and the entire fault judgment process can be performed in parallel with inductance measurement calculations. Moreover, it eliminates the need for resistance value calculations, reducing the computational load of the entire process and avoiding the situation in existing technologies where inaccurate resistance value calculations lead to abnormal fault information judgments. This improves the accuracy of fault judgment and has a significant effect on enhancing the stability of the landing gear retraction and extension control system.

[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram illustrating the principle of sensor inductance and resistance measurement. Figure 2 This is a flowchart illustrating a proximity sensor fault determination method according to this application; Figure 3 Open circuit schematic for measuring the inductance and resistance of a sensor; Figure 4 A schematic diagram of the voltage waveform when the open switch of the measurement circuit is at position 2; Figure 5 A schematic diagram showing the sampling of 2N points when the open switch of the measurement circuit is located at position 2. Figure 6 Schematic diagram of short circuit for measuring sensor inductance and resistance values; Figure 7 A schematic diagram of the voltage waveform when the short-circuit switch of the measurement circuit is at position 2; Figure 8 A schematic diagram showing the sampling of 2N points when the short-circuit switch of the measurement circuit is located at position 2. Figure 9 This is a schematic diagram of the voltage waveform when the circuit is normally connected and the switch is at position 2. Figure 10 A schematic diagram showing N sampling points when the switch is at position 2 in a normally connected circuit. Figure 11 This diagram illustrates how to match the phase of sampling points when the circuit is open to the phase of sampling points when the circuit is normally connected. Figure 12 A schematic diagram showing how to match the phase of sampling points during a short circuit with the phase of sampling points during a normal connection; Figure 13 This diagram illustrates the correlation between sampling points when the circuit is normally connected and when the circuit is open. Figure 14 This diagram illustrates the correlation between sampling points when the circuit is normally connected and when it is short-circuited. Figure 15 This is a schematic diagram of the structure of a proximity sensor fault detection device according to this application; Figure 16 This is a schematic diagram of the structure of an electronic device according to this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this application, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0026] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0027] Figure 2 This is a flowchart illustrating a proximity sensor fault diagnosis method provided in an embodiment of this application. The method is applied to a landing gear retraction and extension control system. Figure 2 As shown, the method includes the following steps: S1. Obtain the first calibration sampling sequence under the open circuit state and the second calibration sampling sequence under the short circuit state, and obtain the operating sampling sequence under the normal connection state of the sensor; In an optional embodiment of this application, step S1 acquires a first calibration sampling sequence under open-circuit conditions and a second calibration sampling sequence under short-circuit conditions, and acquires an operating sampling sequence under normal sensor connection conditions, including: Triggered by the falling edge of the square wave excitation signal, 2N sampling points in the open-circuit state of the acquisition circuit are collected to generate the first calibration sampling sequence; and triggered by the falling edge of the square wave excitation signal, 2N sampling points in the short-circuit state of the acquisition circuit are collected to generate the second calibration sampling sequence. Triggered by the rising edge of the square wave excitation signal, N sampling points are collected under the normal connection state of the sensor to generate the running sampling sequence; where N is a positive integer.

[0028] like Figure 1The diagram shows the schematic of a circuit for measuring the inductance and resistance of a sensor. This circuit mainly includes an AC excitation source module, a sensor interface network module, a state switching switch module, and a signal acquisition and processing module. The AC excitation source module includes an AC voltage source (AC), with its first terminal connected to a common reference ground and its second terminal outputting an alternating excitation signal. The sensor interface network module consists of a reference resistor (R) and the sensor under test (SUT) forming a series branch. The first terminal of the reference resistor (R) is connected to the second terminal of the AC voltage source (AC), and the second terminal of the reference resistor (R) is connected to an intermediate node. The first terminal of the SUT is connected to the intermediate node, and the second terminal of the SUT is connected to the common reference ground. The state switching switch module includes a single-pole double-throw (SPD) switch. The common terminal of the SPD switch is connected to the input terminal of the signal acquisition and processing module. The first throw (position 1) of the SPD switch is connected to the first terminal of the reference resistor (R), and the second throw (position 2) of the SPD switch is connected to the intermediate node.

[0029] like Figure 3 As shown, in this embodiment, the measurement circuit is open (i.e., the circuit is not connected to the sensor). The voltage waveform when the switch is at the second throw position 2 is as follows: Figure 4 As shown; when sampling the falling edge of the standard square wave, as... Figure 5 As shown, the voltage is sampled when the switch is in the second throw position 2, and a total of 2N points are sampled ( , ... The first calibration sampling sequence is generated by sampling N points within one square wave cycle. Specifically, within one cycle of a standard square wave, N points are uniformly sampled from the voltage at the switch location. N is a power of 2, with a minimum value of 64 and a maximum value determined by the upper limit of the sampling rate. Specifically, N must be greater than the number of sampling points contained in half a cycle of the square wave to ensure that the extracted N points can completely cover the entire physical process of the waveform from transient to steady state (i.e., at least one complete peak and subsequent decay edge). Using a power of 2 matches the bit width of the shift register in the underlying FPGA / DSP processor, thereby improving bit operation efficiency.

[0030] like Figure 6 As shown, in this embodiment, the measurement circuit is short-circuited (i.e., the circuit is not connected to the sensor, and the two terminals are directly short-circuited). The voltage waveform when the switch is in the second throw position 2 is as follows: Figure 7 As shown; when sampling the falling edge of the standard square wave, as... Figure 8 As shown, the voltage is sampled when the switch is in the second throw position 2, and a total of 2N points are sampled ( , ... ), generating the second calibration sampling sequence.

[0031] like Figure 1As shown in the figure, when the sensor is normally connected in this embodiment, the voltage waveform when the switch is in the second throw position 2 is as follows: Figure 9 As shown; when measuring the inductance value using the rising edge of a standard square wave, as... Figure 10 As shown, the voltage is sampled when the switch is in the second throw position 2, and a total of N points are sampled ( , ... This means that the sampling points here can be the same as the inductance value being measured, and a running sampling sequence can be generated.

[0032] This embodiment employs a differentiated sampling strategy of 'acquiring the falling edge for open / short circuit references and acquiring the rising edge for normal connections'. Its core purpose is to achieve deep collaboration and resource reuse with the inductance measurement hardware loop of airborne equipment. Since the rising edge of the square wave in the actual driving circuit of the proximity sensor typically corresponds to the charging / excitation phase (for inductance measurement), while the falling edge corresponds to the discharging / reset phase, this embodiment arranges the reference sampling for fault judgment (open / short circuit) on the falling edge and the real-time status sampling on the rising edge. This staggered timing collaboration allows the fault detection module and the inductance measurement module to share the same AD sampling hardware and square wave excitation source without interfering with each other. This not only saves valuable airborne hardware resources (eliminating the need for additional fault detection excitation sources) but also enables parallel processing of fault judgment and inductance calculation, significantly improving the system's real-time performance and integration.

[0033] S2. Determine the peak positions of the first calibration sampling sequence, the second calibration sampling sequence, and the running sampling sequence, respectively; In an optional embodiment of this application, step S2, determining the peak positions of the first calibration sampling sequence, the second calibration sampling sequence, and the running sampling sequence, includes: The first calibration sampling sequence, the second calibration sampling sequence, and the running sampling sequence are traversed respectively to find the maximum value of the voltage amplitude; The index position corresponding to the maximum point is determined as the peak position of the corresponding sampling sequence.

[0034] like Figure 5 As shown, this embodiment identifies the sampling points of the first calibration sampling sequence. , ... The point where the first peak appears in the graph is denoted as x, and this point is recorded as . .

[0035] like Figure 8 As shown, this embodiment identifies the sampling points of the second calibration sampling sequence. , ... The point where the first peak appears in the graph is denoted as y, and this point is recorded as . .

[0036] like Figure 10 As shown, this embodiment identifies the sampling points of the running sampling sequence. , ... The point where the first peak appears in the graph is denoted as z. .

[0037] S3. Based on the peak position, extract target sampling point sequences that are phase-aligned with the running sampling sequence from the first calibration sampling sequence and the second calibration sampling sequence, respectively; In an optional embodiment of this application, step S3, based on the peak position, extracts target sampling point sequences that are phase-aligned with the running sampling sequence from the first calibration sampling sequence and the second calibration sampling sequence, respectively, including: The location of the first peak in the running sampling sequence is determined as the first reference position; The positions of the first peaks in the first calibration sampling sequence and the second calibration sampling sequence are respectively determined as the second reference position and the third reference position; Based on the offset relationship between the first reference position, the second reference position, and the third reference position, in the first calibration sampling sequence and the second calibration sampling sequence, offset the first reference position by a number of sampling points in the opposite direction of time increment, as the starting point for truncation; N sampling points are continuously extracted from the starting point of the extraction, which are used as the target sampling point sequence that is phase-aligned with the running sampling sequence; where N is a positive integer.

[0038] Specifically, based on the offset relationship between the first reference position, the second reference position, and the third reference position, sampling points offset from the corresponding reference position in the first calibration sampling sequence and the second calibration sampling sequence in the opposite direction of time increment are used as the starting point for truncation, including: For the first calibrated sampling sequence, the index value of the second reference position is subtracted from the index value of the first reference position to obtain the first truncation starting point; For the second calibration sampling sequence, the index value of the third reference position is subtracted from the index value of the first reference position to obtain the second truncation starting point.

[0039] like Figure 11 As shown, this embodiment is based on and Based on the positional relationship, discard the redundant sampling points in the first calibration sampling sequence, and then select the 2N sampling points in the open circuit ( , ... Extract N points from ) , ... ), ensuring that the location of the peak in these N sampling points is consistent with They are in the same location.

[0040] The specific operation is as follows: According to The position z is in ( , ... )middle At position x, take z points forward (in the opposite direction of time increment), then from position xz, take N points backward (in the direction of time increment). , ... ), and discard the excess sampling points; relabel these N points as ( , ... ), generating the first target sampling point sequence to match the phase of the sampling points when the sensor is normally connected (because The peak must be located within N / 2 points, therefore, out of 2N sampling points, N points can definitely be extracted to match its phase.

[0041] like Figure 12 As shown, according to and Based on the positional relationship, redundant sampling points in the second calibration sampling sequence are discarded, and the remaining 2N sampling points are short-circuited. , ... Extract N points from ) , ... ), ensuring that the location of the peak in these N sampling points is consistent with They are in the same location.

[0042] The specific operation is as follows: According to The position z is in ( , ... )middle At position y, take z points forward (in the opposite direction of time increment), then from position yz, take N points backward (in the direction of time increment). , ... ), and discard the excess sampling points; relabel these N points as ( , ... This generates a second target sampling point sequence to match the phase of the sampling points when the sensor is normally connected (because...). The peak must be located within N / 2 points, therefore, out of 2N sampling points, N points can definitely be extracted to match its phase.

[0043] When the circuit is open or short-circuited, sampling is initiated by sampling the falling edge of the standard square wave, and 2N points are sampled. When the sensor is normally connected, sampling is initiated by sampling the rising edge of the standard square wave, and N points are sampled. By identifying the peak positions of these sampling points, the redundant sampling points when the circuit is open or short-circuited are automatically shielded, and the phase of the sampling points when the circuit is open or short-circuited is adjusted to the phase of the sampling points when the sensor is normally connected, forming a new set of N sampling points, so that the phase and number of the sampling points to be compared are completely consistent.

[0044] The 2N-point sampling performed when the circuit is open or short-circuited is exactly the same as the N-point sampling when the sensor is normally connected, using the same square wave signal, sampling circuit, and sampling rate. The only difference is the timing of sampling and the number of sampling points. The advantage is that this method does not require a separate measurement circuit design and can be performed in parallel with inductance value calculation. When the circuit is measuring inductance, the existing sampling points can be used to determine whether an open or short circuit fault has occurred.

[0045] S4. Calculate the normalized mean square error between the running sampling sequence and the two target sampling point sequences; In an optional embodiment of this application, step S4, calculating the normalized mean square error of the running sampling sequence and the two target sampling point sequences, includes: The difference between each sampling point in the target sampling point sequence and the corresponding sampling point in the running sampling sequence is calculated, the squares are obtained, and the sums are accumulated to obtain the sum of squared errors. The squares of each sampling point in the target sampling point sequence are summed to obtain the baseline sum of squares. The ratio of the sum of squared errors to the sum of squared references is taken as the normalized mean square error.

[0046] like Figure 13 As shown, this embodiment is based on sampling points ( , ... ) and extraction point ( , ... ), calculate the degree of correlation between the sampling points when the sensor is normally connected and the sampling points when the circuit is open. ,in To calculate the k-th normalized mean square error between the sampled sequence and the first target sampled point sequence, For the i-th sampling point in the first target sampling point sequence, This refers to the i-th sampling point in the running sampling sequence.

[0047] like Figure 14 As shown, this embodiment is based on sampling points ( , ... ) and extraction point ( , ... ), calculate the degree of correlation between the sampling points when the sensor is normally connected and the sampling points when the circuit is short-circuited. ,in To calculate the k-th normalized mean square error between the sampled sequence and the second target sampled point sequence, This refers to the i-th sampling point in the second target sampling point sequence.

[0048] Because the system uses square wave excitation, and there is an inherent hardware delay difference between the triggering timing of open / short circuit reference sampling (falling edge) and real-time sampling (rising edge), the directly sampled waveform has a phase shift on the time axis. If the NMSE is calculated directly, the calculation result will mainly reflect the 'phase difference' rather than the 'waveform distortion', leading to misjudgment of faults. This embodiment cleverly uses the 'first peak' as a natural physical time anchor point, and by dynamically extracting N points from 2N points, the phase shift caused by the hardware trigger delay is forcibly eliminated in the time domain. Only under the premise of strict phase alignment can NMSE accurately capture the high-frequency transient waveform distortion and amplitude attenuation caused by the distributed capacitance / inductance of long cables. This combination of 'time domain alignment first, then NMSE quantization' completely avoids the phase sensitivity defect of traditional frequency domain analysis (such as DFT) and achieves high-precision fault identification under asynchronous sampling.

[0049] S5. Based on the relationship between the normalized mean square error and the preset normal range, determine whether the sensor has an open circuit fault or a short circuit fault.

[0050] In an optional embodiment of this application, step S5 determines whether the sensor has an open-circuit fault or a short-circuit fault based on the relationship between the normalized mean square error and the preset normal range, including: When the normalized mean square error of the running sampling sequence relative to the first calibrated sampling sequence is less than the lower limit of the preset normal range, it is determined that the sensor has an open circuit fault. When the normalized mean square error of the running sampling sequence relative to the second calibrated sampling sequence is greater than the upper limit of the preset normal range, the sensor is determined to have a short circuit fault.

[0051] The method for constructing the preset normal interval includes: Under the condition that the sensor is properly connected and not malfunctioning, multiple sets of operational sampling sequences are collected; The multiple sets of running sampling sequences are combined in pairs, and the normalized mean square error corresponding to each pair of combinations is calculated. Extract the minimum and maximum values ​​of the normalized mean square error corresponding to all combinations, and construct the preset normal interval.

[0052] Specifically, the multiple sets of sampled sequences are combined pairwise, and the normalized mean square error corresponding to each pair of combinations is calculated, including: For any two sets of running sampling sequences, one set of running sampling sequences is used as the reference sequence, and the other set of running sampling sequences is used as the comparison sequence; The normalized mean square error of the alignment sequence relative to the reference sequence is calculated as the normalized mean square error corresponding to the two combinations.

[0053] When the sensor is properly connected and not malfunctioning, such as Figure 1 As shown, when the standard square wave rises, the voltage is sampled when the switch is in the second throw position 2. N points are sampled as a group, and one group is sampled each time power is applied, for a total of 20 groups. These 20 groups of waveforms are then combined pairwise, resulting in 190 combinations. The normalized mean square error (MSE) of each of these 190 combinations is calculated, and the minimum value of the MSE can be obtained. and maximum value Then the normal range can be set to .Compare Is it less than If yes, then the sensor has an open circuit fault; otherwise, no open circuit fault has occurred. Is it greater than If so, the sensor has a short circuit fault; otherwise, no short circuit fault has occurred. This embodiment calibrates the preset normal range using a combination of Monte Carlo statistics and extreme condition envelope methods. In specific implementation, waveforms of 20 normal sensors are collected under extreme temperatures (-55℃ to +125℃) and different cable lengths (1 meter to 15 meters), and 190 combinations of NMSE values ​​are calculated. The minimum and maximum values ​​of their normalized mean square error are taken as the normal range. This statistically based range setting gives this embodiment a clear fault tolerance boundary when facing common-mode interference and temperature drift in long cables, avoiding the 'critical state jump' problem caused by traditional single fixed thresholds.

[0054] Figure 15This is a schematic diagram of a proximity sensor fault diagnosis device provided in an embodiment of this application. The device is applied to a landing gear retraction and extension control system. Figure 15 As shown, the device includes: The sequence acquisition module is used to acquire the first calibration sampling sequence under the open circuit state and the second calibration sampling sequence under the short circuit state, and to acquire the operating sampling sequence under the normal connection state of the sensor. The peak determination module is used to determine the peak positions of the first calibration sampling sequence, the second calibration sampling sequence, and the running sampling sequence, respectively. The alignment and extraction module is used to extract target sampling point sequences that are phase-aligned with the running sampling sequence from the first calibration sampling sequence and the second calibration sampling sequence, respectively, based on the peak position. An error calculation module is used to calculate the normalized mean square error between the running sampling sequence and the two target sampling point sequences; The fault diagnosis module is used to determine whether the sensor has an open circuit fault or a short circuit fault based on the relationship between the normalized mean square error and the preset normal range.

[0055] The proximity sensor fault determination device provided in this application has the same technical features as the proximity sensor fault determination method provided in the above embodiments, so it can also solve the same technical problems and achieve the same technical effects.

[0056] like Figure 16 As shown, this application provides an electronic device, including: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed, enables the at least one processor to perform the proximity sensor fault determination method provided in the above embodiments.

[0057] See Figure 16 Electronic devices also include: a bus and a communication interface, with the processor, communication interface and memory connected via the bus; the processor is used to execute executable modules stored in the memory, such as computer programs.

[0058] The memory may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0059] The bus can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 16 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0060] The memory is used to store programs. After receiving an execution instruction, the processor executes the program. The method executed by the apparatus defined by the process disclosed in any of the foregoing embodiments of this application can be applied to the processor or implemented by the processor.

[0061] The processor may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0062] Corresponding to the above-described intelligent optimization control method for mineral processing, this application also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the proximity sensor fault judgment method provided in the above embodiments.

[0063] This application also provides a landing gear retraction and extension control system, including: Proximity sensors are used to detect the locking status of landing gear mechanical components; A controller, connected to the proximity sensor, is configured to execute the proximity sensor fault determination method described above to output an open-circuit fault or short-circuit fault status signal for the proximity sensor.

[0064] The intelligent optimization control device for the mineral processing process provided in this application embodiment can be specific hardware on the equipment or software or firmware installed on the equipment. The implementation principle and technical effects of the device provided in this application embodiment are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0065] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0066] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0067] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0068] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0069] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the intelligent optimization control method for the mineral processing process described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0070] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0071] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for diagnosing proximity sensor faults, characterized in that, Includes the following steps: Acquire the first calibration sampling sequence under the open circuit state and the second calibration sampling sequence under the short circuit state, and acquire the operating sampling sequence under the normal sensor connection state; The peak positions of the first calibration sampling sequence, the second calibration sampling sequence, and the running sampling sequence are determined respectively; Based on the peak position, target sampling point sequences aligned with the phase of the running sampling sequence are extracted from the first calibration sampling sequence and the second calibration sampling sequence, respectively; Calculate the normalized mean square error between the running sampling sequence and the two target sampling point sequences; Based on the relationship between the normalized mean square error and the preset normal range, it is determined whether the sensor has an open circuit fault or a short circuit fault.

2. The proximity sensor fault diagnosis method according to claim 1, characterized in that, Acquire the first calibration sampling sequence under open-circuit conditions and the second calibration sampling sequence under short-circuit conditions, and acquire the operating sampling sequence under normal sensor connection conditions, including: Triggered by the falling edge of the square wave excitation signal, 2N sampling points in the open-circuit state of the acquisition circuit are collected to generate the first calibration sampling sequence; and triggered by the falling edge of the square wave excitation signal, 2N sampling points in the short-circuit state of the acquisition circuit are collected to generate the second calibration sampling sequence. Triggered by the rising edge of the square wave excitation signal, N sampling points are collected under the normal connection state of the sensor to generate the running sampling sequence; where N is a positive integer.

3. The proximity sensor fault diagnosis method according to claim 1, characterized in that, Calculating the normalized mean square error of the running sampling sequence and the two target sampling point sequences includes: The difference between each sampling point in the target sampling point sequence and the corresponding sampling point in the running sampling sequence is calculated, the squares are obtained, and the sums are accumulated to obtain the sum of squared errors. The squares of each sampling point in the target sampling point sequence are summed to obtain the baseline sum of squares. The ratio of the sum of squared errors to the sum of squared references is taken as the normalized mean square error.

4. The proximity sensor fault diagnosis method according to claim 1, characterized in that, Based on the relationship between the normalized mean square error and the preset normal range, it is determined whether the sensor has an open-circuit fault or a short-circuit fault, including: When the normalized mean square error of the running sampling sequence relative to the first calibrated sampling sequence is less than the lower limit of the preset normal range, it is determined that the sensor has an open circuit fault. When the normalized mean square error of the running sampling sequence relative to the second calibrated sampling sequence is greater than the upper limit of the preset normal range, the sensor is determined to have a short circuit fault.

5. The proximity sensor fault diagnosis method according to claim 1, characterized in that, Based on the peak position, target sampling point sequences aligned with the phase of the running sampling sequence are extracted from the first calibration sampling sequence and the second calibration sampling sequence, respectively, including: The location of the first peak in the running sampling sequence is determined as the first reference position; The positions of the first peaks in the first calibration sampling sequence and the second calibration sampling sequence are respectively determined as the second reference position and the third reference position; Based on the offset relationship between the first reference position, the second reference position, and the third reference position, in the first calibration sampling sequence and the second calibration sampling sequence, offset the first reference position by a number of sampling points in the opposite direction of time increment, as the starting point for truncation; N sampling points are continuously extracted from the starting point of the extraction, which are used as the target sampling point sequence that is phase-aligned with the running sampling sequence; where N is a positive integer.

6. The proximity sensor fault diagnosis method according to claim 5, characterized in that, Based on the offset relationship between the first reference position, the second reference position, and the third reference position, in the first calibration sampling sequence and the second calibration sampling sequence, offset by a number of sampling points from the corresponding reference position in the opposite direction of time increment, as the starting point for truncation, including: For the first calibrated sampling sequence, the index value of the second reference position is subtracted from the index value of the first reference position to obtain the first truncation starting point; For the second calibration sampling sequence, the index value of the third reference position is subtracted from the index value of the first reference position to obtain the second truncation starting point.

7. The proximity sensor fault diagnosis method according to claim 2 or 5, characterized in that, The rules for choosing the value of N include: N takes the value of an integer power of 2, and the minimum value of N is 64.

8. The proximity sensor fault diagnosis method according to claim 1, characterized in that, The method for constructing the preset normal range includes: Under the condition that the sensor is properly connected and not malfunctioning, multiple sets of operational sampling sequences are collected; The multiple sets of running sampling sequences are combined in pairs, and the normalized mean square error corresponding to each pair of combinations is calculated. Extract the minimum and maximum values ​​of the normalized mean square error corresponding to all combinations to construct the preset normal interval.

9. The proximity sensor fault diagnosis method according to claim 8, characterized in that, The multiple sets of sampled sequences are combined pairwise, and the normalized mean square error corresponding to each pair of combinations is calculated, including: For any two sets of running sampling sequences, one set of running sampling sequences is used as the reference sequence, and the other set of running sampling sequences is used as the comparison sequence; The normalized mean square error of the alignment sequence relative to the reference sequence is calculated as the normalized mean square error corresponding to the two combinations.

10. The proximity sensor fault diagnosis method according to claim 1, characterized in that, Determining the peak positions of the first calibration sampling sequence, the second calibration sampling sequence, and the running sampling sequence, respectively, includes: The first calibration sampling sequence, the second calibration sampling sequence, and the running sampling sequence are traversed respectively to find the maximum value of the voltage amplitude; The index position corresponding to the maximum point is determined as the peak position of the corresponding sampling sequence.

11. A proximity sensor fault detection device, characterized in that, The proximity sensor fault determination method as described in any one of claims 1 to 10 includes: The sequence acquisition module is used to acquire the first calibration sampling sequence under the open circuit state and the second calibration sampling sequence under the short circuit state, and to acquire the operating sampling sequence under the normal connection state of the sensor. The peak determination module is used to determine the peak positions of the first calibration sampling sequence, the second calibration sampling sequence, and the running sampling sequence, respectively. The alignment and truncation module is used to extract target sampling point sequences that are phase-aligned with the running sampling sequence from the first calibration sampling sequence and the second calibration sampling sequence, respectively, based on the peak position. An error calculation module is used to calculate the normalized mean square error between the running sampling sequence and the two target sampling point sequences; The fault diagnosis module is used to determine whether the sensor has an open circuit fault or a short circuit fault based on the relationship between the normalized mean square error and the preset normal range.

12. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the proximity sensor fault determination method as described in any one of claims 1 to 10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the proximity sensor fault determination method as described in any one of claims 1 to 10.

14. A landing gear retraction and extension control system, characterized in that, include: Proximity sensors are used to detect the locking status of landing gear mechanical components; A controller, connected to the proximity sensor, is configured to perform the proximity sensor fault determination method as described in any one of claims 1 to 10, to output an open-circuit fault or short-circuit fault status signal of the proximity sensor.

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