Diagnostic system

Abstract

A diagnostic system for diagnosing an abnormality of a passenger conveyor that conveys passengers by cyclically moving a plurality of steps linked in a ring shape, includes a diagnostic step included in the plurality of steps, having one or more diagnostic sensors including a magnetic sensor, a magnetic profile generation section for generating a magnetic profile representing a correlation between a position in the passenger conveyor during one cycle of the diagnostic step and a detection value of the magnetic sensor, a position estimation section for estimating a position in the passenger conveyor at which the detection value of the diagnostic sensor is detected, based on the magnetic profile and the detection value of the magnetic sensor at the time of cyclic movement, and an abnormality diagnosis section for performing an abnormality diagnosis based on the detection value of the diagnostic sensor.

Description

Technical Field

[0001] This invention relates to a diagnostic system for passenger conveyors. Background Technology

[0002] Patent Document 1 describes a structure that mounts sensors inside the diagnostic steps and performs measurements inside the escalator by circulating the diagnostic steps within the escalator. For example, during periodic inspections, the efficiency of these inspections can be improved by replacing the regular steps with diagnostic steps. Because the diagnostic steps circulate within the escalator, it is necessary to ensure that the time of data acquisition corresponds to the current position of the diagnostic steps. In Patent Document 1, an accelerometer is used to obtain the moment when the diagnostic steps are pulled to the back side of the escalator and reverse, and the step position is calculated based on the elapsed time from the start of the reversal.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2006-76729 Summary of the Invention

[0006] The technical problem that the invention aims to solve

[0007] However, the technology described in Patent Document 1 is based on the premise of constant speed operation of escalators, so it is difficult to apply to escalators that reduce operating speed to save electricity when there are no passengers.

[0008] Technical means for solving technical problems

[0009] The diagnostic system of the present invention diagnoses anomalies in a passenger conveyor that transports passengers by cyclically moving multiple steps connected in a loop. The diagnostic system includes: a diagnostic step, which includes at least one diagnostic sensor, including a magnetic sensor, among the multiple steps; a magnetic map generation unit for generating a magnetic map showing the correlation between the position in the passenger conveyor during one cycle of the diagnostic step and the detection value of the magnetic sensor; a position estimation unit for estimating the position in the passenger conveyor where the detection value of the diagnostic sensor is detected, based on the magnetic map and the detection value of the magnetic sensor during cyclic movement; and an anomaly diagnosis unit for performing anomaly diagnosis based on the detection value of the diagnostic sensor.

[0010] The effects of the invention

[0011] According to the present invention, even under conditions of varying operating speed, the detection position of the diagnostic sensor can be accurately calculated. Attached Figure Description

[0012] Figure 1 This is a schematic diagram showing the general structure of an escalator.

[0013] Figure 2 It is a three-dimensional diagram showing the approximate structure of the diagnostic ladder.

[0014] Figure 3 This is a functional block diagram of the diagnostic system.

[0015] Figure 4 This is a diagram illustrating an example of noise reduction processing.

[0016] Figure 5 This is a diagram illustrating an example of the location of diagnostic ladders.

[0017] Figure 6 This is a figure representing an example of time-series magnetic detection data after noise reduction processing.

[0018] Figure 7 This is a diagram illustrating the second method corresponding to the location.

[0019] Figure 8 This is a diagram illustrating an example of how one's own position is calculated.

[0020] Figure 9 This is a flowchart representing an example of an anomaly detection action.

[0021] Figure 10 This diagram illustrates an example of how to determine the occurrence and signs of anomalies.

[0022] Figure 11 This is a block diagram representing a modified example 1 of the structure of a diagnostic system.

[0023] Figure 12 This is a block diagram representing a modified example 2 of the structure of a diagnostic system.

[0024] Figure 13 This is a block diagram representing a modified example 3 of the structure of a diagnostic system.

[0025] Figure 14 This is a block diagram representing a modified example 4 of the structure of a diagnostic system.

[0026] Figure 15 This is a block diagram representing a modified example 5 of the structure of a diagnostic system.

[0027] Figure 16 This is a block diagram representing a modified example 6 of the structure of a diagnostic system.

[0028] Figure 17 This is a block diagram representing a modified example 7 of the structure of a diagnostic system. Detailed Implementation

[0029] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. The embodiments are examples used to illustrate the present invention, and appropriate omissions and simplifications have been made for clarity. The present invention can also be implemented in various other ways. Unless otherwise specified, each constituent element can be single or multiple. The positions, sizes, shapes, and extents of the constituent elements shown in the figures do not always represent actual positions, sizes, shapes, or extents for ease of understanding. Therefore, the present invention is not limited to the positions, sizes, shapes, and extents shown in the drawings. When multiple constituent elements have the same or identical function, different suffixes are sometimes added to the same reference numerals for description. Furthermore, when it is not necessary to distinguish between the multiple constituent elements, suffixes are sometimes omitted in the description.

[0030] Figure 1 This diagram illustrates an example of a passenger conveyor, the object of diagnosis for the diagnostic system of the present invention. In this embodiment, the passenger conveyor is an escalator. Figure 1 This is a schematic diagram showing the general structure of the escalator 100. The escalator 100 includes steps 1 and 1a, chain 2, frame 3, terminal gear 4, drive motor 6, lower terminal gear 9, handrail 8, guide rails 11a and 11b, control device 12, upper passenger / passage floor 13a, and lower passenger / passage floor 13b. The terminal gear 4, drive motor 6, lower terminal gear 9, and control device 12 are housed within the frame 3.

[0031] In the escalator 100, multiple steps 1 and one diagnostic step 1a are connected in a loop by a circular chain 2. The escalator 100 transports passengers by cyclically moving the multiple steps 1 and the diagnostic step 1a connected in the loop. Furthermore, Figure 1 In the diagram, a shading line is applied to the diagnostic step 1a. The upper passenger stop floor 13a is a steel plate that forms the ground of the upper passenger stop of the escalator 100. The lower passenger stop floor 13b is a steel plate that forms the ground of the lower passenger stop of the escalator 100.

[0032] A terminal gear 4, engaged with the chain 2, is installed inside the housing frame 3 on the lower side of the upper passenger / passenger landing floor 13a. When the chain 2 is driven by this terminal gear 4, each step 1, 1a connected to the chain 2 moves cyclically between the upper passenger / passenger landing floor 13a and the lower passenger / passenger landing floor 13b. Additionally, the handrail 8 cyclically moves synchronously with the chain 2. The terminal gear 4 is driven by a drive motor 6 having a drive gear 5. The drive motor 6 is controlled by a control device 12. A drive chain belt 7 is mounted between the drive gear 5 and the terminal gear 4. A lower terminal gear 9 is installed inside the housing frame 3 on the lower side of the lower passenger / passenger landing floor 13b. The chain 2 engages with this lower terminal gear 9.

[0033] The sides of each step 1, 1a are roughly fan-shaped. A front guide roller 10a and a rear guide roller 10b are provided on each step 1, 1a. A pair of front guide rollers 10a and a pair of rear guide rollers 10b are provided on each side of each step 1, 1a, i.e., on both sides of the paper. Inside the housing frame 3, guide rails 11a for the front guide rollers 10a and guide rails 11b for the rear guide rollers 10b are provided. A pair of guide rails 11a and 11b are provided at the top and bottom of the housing frame 3.

[0034] As steps 1 and 1a move from the lower terminal gear 9 to the terminal gear 4, the front guide roller 10a and the rear guide roller 10b travel on the upper guide rails 11a and 11b. Upon reaching the terminal gear 4, steps 1 and 1a move along the terminal gear 4. As steps 1 and 1a move along the terminal gear 4 from the upper side to the lower side (as shown in the diagram), their orientation reverses. After reversing, the front guide roller 10a and the rear guide roller 10b of steps 1 and 1a transfer to the lower guide rails 11a and 11b. As steps 1 and 1a move from the terminal gear 4 to the lower terminal gear 9, the front guide roller 10a and the rear guide roller 10b travel on the lower guide rails 11a and 11b. Upon reaching the lower terminal gear 9, steps 1 and 1a move along the lower terminal gear 9, and their orientation reverses again. After being reversed, the front guide roller 10a and the rear guide roller 10b of the steps 1 and 1a are transferred to the upper guide rails 11a and 11b.

[0035] Figure 2 This is a perspective view showing the schematic structure of a diagnostic step 1a. The diagnostic step 1a has a passenger-accessible tread (pedal section) 21, a kick plate section 22 continuous with the tread section 21, and a pair of side sections 23. A front guide roller 10a and a rear guide roller 10b are provided on each side section 23. A sensor terminal 24 is provided within the diagnostic step 1a. A sensor section 25, a control section 26, and a wireless communication section 27 are provided within the sensor terminal 24. Furthermore, the structure of step 1 is the same as that of the diagnostic step 1a, except that it does not include the sensor terminal 24.

[0036] Figure 3 This is a functional block diagram illustrating the functional structure of the diagnostic system of this embodiment. The diagnostic system 1000 includes: a diagnostic step 1a and a control device 12 installed on the escalator 100, a position calculation device 51 of a monitoring center 50 that performs remote diagnostic processing, an anomaly diagnostic device 52, and a communication device 53. The data collection device 30 transmits and receives data with the monitoring center 50 via a network 40.

[0037] As described above, a sensor terminal 24 is installed within the diagnostic step 1a. The sensor terminal 24 includes a sensor unit 25, a control unit 26, and a wireless communication unit 27. The sensor unit 25 includes a magnetic sensor 251, a sound sensor 252, and an acceleration sensor 253 as diagnostic sensors. The control device 12, installed within the frame 3 of the escalator 100, includes a data collection device 30 comprising a data storage unit 301 and a communication unit 302. Magnetic detection data from the magnetic sensor 251, sound detection data from the sound sensor 252, and acceleration detection data from the acceleration sensor 253 are transmitted to the data collection device 30 via the wireless communication unit 27. The wireless communication unit 27 uses low-power, low-cost, short-range wireless communication. A battery is used as the power source for the sensor terminal 24, and the battery is replaced if necessary, for example, during periodic inspections.

[0038] As a data collection method, for example, timing detection data during one revolution of the diagnostic ladder 1a is temporarily stored in a memory (not shown) included in the control unit 26. Then, when a quantity of timing detection data from multiple revolutions has accumulated in the memory, this quantity of timing detection data is transmitted via the wireless communication unit 27. Hereinafter, the quantity of timing detection data from multiple revolutions is referred to as a timing detection data set. The data transmission performed by the wireless communication unit 27 is repeated at predetermined time intervals. The data collection device 30 stores the timing detection data sets received via the communication unit 302 in the data storage unit 301.

[0039] Multiple sets of timing detection data accumulated in the data storage unit 301 are sent to the monitoring center 50 at predetermined intervals. This transmission is executed according to a transmission command from the monitoring center 50. For example, every 24 hours, a transmission command for the timing detection data sets is output from the monitoring center 50. When the data collection device 30 receives the transmission command from the monitoring center 50, it sends 24 hours' worth of timing detection data sets accumulated in the data storage unit 301 to the monitoring center 50. The multiple sets of timing detection data sent from the communication unit 302 are received by the communication device 53 of the monitoring center 50 via the network 40.

[0040] The monitoring center 50 includes a position estimation device 51, an anomaly diagnosis device 52, and a communication device 53. The position estimation device 51 includes a magnetic distribution map generation unit 511, a self-position estimation unit 512, a noise reduction unit 513, and a storage unit 514. The noise reduction unit 513 performs noise reduction processing on the time-series detection data (magnetic detection data, sound detection data, and acceleration detection data) used when generating the magnetic distribution map. Details of the noise reduction processing are described later.

[0041] In the magnetic distribution map generation unit 511, a magnetic distribution map is generated based on the noise-reduced temporal magnetic detection data. Specifically, for a quantity of temporal magnetic detection data covering one revolution, the detection values ​​at each moment are correlated with their positions within the escalator 100, thereby generating a magnetic distribution map. The magnetic distribution map generation process will be described later. The magnetic distribution map generation process is performed as an initial operation when diagnostic step 1a is installed on the escalator 100. The generated magnetic distribution map is stored in the storage unit 514.

[0042] As described below, the self-position estimation unit 512, based on the time-series magnetic detection data and magnetic distribution map, correlates each detection value of the time-series sound detection data and time-series acceleration detection data used for anomaly diagnosis with each position of the escalator 100. Through self-position estimation processing, it can be determined at which position of the escalator 100 the sound detection data and acceleration detection data detected simultaneously with specific data in the magnetic detection data were detected. Details of the self-position estimation processing of the self-position estimation unit 512 will be described later.

[0043] The anomaly diagnosis device 52 performs anomaly diagnosis on the escalator 100 based on the magnetic distribution map and detection data from diagnostic sensors. The anomaly diagnosis device 52 includes an anomaly calculation unit 521, a noise reduction unit 522, a storage unit 523, and a reporting unit 524. The anomaly calculation unit 521 performs anomaly calculation processing (described later) based on sound detection data processed by the self-position calculation unit 512. The noise reduction unit 522 performs noise reduction processing on the timing detection data set used for anomaly diagnosis. The reporting unit 524 performs a reporting action when the anomaly calculation unit 521 determines an anomaly. Upon receiving an anomaly report based on the reporting action, the operator performs an inspection of the escalator 100. The storage unit 523 stores judgment reference data used for anomaly diagnosis, timing detection data sets acquired from the data collection device 30, etc.

[0044] (Noise reduction processing)

[0045] Figure 4 This is a diagram illustrating an example of the noise reduction processing of noise reduction units 513 and 522. Here, the example is given using time-series magnetic detection data contained in the time-series detection data set. Figure 4 This represents the time-series magnetic detection data under the condition that the diagnostic step 1a rotates at a certain speed. Figure 4 In the diagram, the vertical axis represents the detected value, and the horizontal axis represents time. Time-series magnetic detection data D1 is the data from the first cycle, D2 is the data from the second cycle, and D3 is the data from the third cycle. Time-series magnetic detection data D2 is shown offset by Δ along the vertical axis compared to time-series magnetic detection data D1. Similarly, time-series magnetic detection data D3 is shown offset by Δ along the vertical axis compared to time-series magnetic detection data D2. T is the time required for one complete cycle, i.e., the period.

[0046] exist Figure 4 In the example shown, noise n1 is generated in the time-series magnetic detection data D1, noise n2 is generated in the time-series magnetic detection data D2, and noise n3 is generated in the time-series magnetic detection data D3. In the noise reduction units 513 and 522, for example, the detection values ​​of the portions generating each of the noise n1 to n3 are reduced by averaging the detection values ​​of the time-series magnetic detection data D1 to D3 over multiple cycles. When averaging the time-series magnetic detection data over 10 cycles, if noise is generated only in 1 cycle, the detection value of the noise portion is reduced to 1 / 10 through averaging. This averaged time-series magnetic detection data is used as the noise-reduced time-series magnetic detection data. Furthermore, the noise reduction processing for the time-series sound detection data and the time-series acceleration detection data is performed in the same way as for the time-series magnetic detection data described above.

[0047] (Magnetic distribution map generation and processing)

[0048] The magnetic distribution map generation unit 511 generates a magnetic distribution map M (magnetic detection value, position) by matching the magnetic detection values ​​at each moment with the positions in the escalator 100 after noise reduction processing by the noise reduction unit 513 for a total of one cycle of temporal magnetic detection data. Various methods exist for matching these values; three methods are described below.

[0049] The first method will be explained below. First, the starting position of the diagnostic step 1a is positioned at a predetermined location. Then, from that position, the diagnostic step 1a is rotated multiple times at a certain speed to obtain... Figure 4 The diagram shows multiple time-series magnetic detection data. Here, we will consider the noise reduction process described above, taking the case of multiple rotations as an example. However, if noise reduction is performed using detection data with a single rotation, the number of rotations (cyclic movement) can also be 1.

[0050] Position the diagnostic step 1a to the designated starting position, for example, as described below. While visually observing the diagnostic step 1a, the operator manually operates the control device 12 to adjust the position of the diagnostic step 1a. Through this adjustment, such as... Figure 5 As shown, the diagnostic step 1a is positioned at the point where it protrudes (turns out) from the lower boarding / alighting floor 13b (hereinafter referred to as reference position A).

[0051] The acquired time-series magnetic detection data are sent to the monitoring center 50, where the aforementioned noise reduction processing is performed in the noise reduction unit 513 of the position estimation device 51. The result is... Figure 6The denoised temporal magnetic detection data D is shown. The horizontal axis represents the elapsed time from the diagnostic step 1a passing the reference position A. The origin O (t=0) corresponds to... Figure 5 The reference position A is shown. The timing magnetic detection data D represents the magnetic field surrounding the diagnostic step 1a during one revolution of the diagnostic step 1a, i.e., the magnetization status of the components of the escalator 100. The cyclically moving diagnostic step 1a reaches the reference position A every cycle T, and the detection value Da is detected by the magnetic sensor 251. Figure 6 At times t = tb, tc, te, and tf, the positions of diagnostic ladder 1a are respectively Figure 5 Positions B, C, E, and F. Return to the reference position A at t = ta.

[0052] The magnetic distribution map generation unit 511 generates a magnetic distribution map that corresponds the time-series magnetic detection data D to each position of the escalator 100, based on the moving speed of the diagnostic step 1a and the elapsed time from the reference position A. That is, the magnetic distribution map M for one loop of the diagnostic step 1a, such as data groups (Da, A), ..., (Db, B), ..., (Dc, C), ..., (De, E), ..., (Df, F), ..., (Da, A), can be represented as a set of data (magnetic detection value, position). In other words, the magnetic distribution map M can be represented as M(magnetic detection value, position).

[0053] The second method will be described below. In the second method, a magnetic distribution map M (magnetic detection value, position) is generated using the detection value of the accelerometer 253 provided in the sensor section 25. Figure 7 This is a graph representing the time-series magnetic detection data D1 and the time-series acceleration detection data D10. The reference numerals A through F correspond to... Figure 5 The positions of the escalator 100 shown are A to F. Figure 5 In the example shown, when observing the posture of the diagnostic ladder 1a, the second posture from position E to position F is reversed vertically compared to the first posture from position A to position B. During the movement of the diagnostic ladder 1a from position B to position E, the posture of the diagnostic ladder 1a gradually changes from the first posture to the second posture. Conversely, during the movement of the diagnostic ladder 1a from position F to position A, the posture of the diagnostic ladder 1a gradually changes from the second posture to the first posture.

[0054] The detection value of the accelerometer 253 installed within the diagnostic step 1a changes according to the movement of the diagnostic step 1a. The acceleration detected by the accelerometer 253 includes acceleration caused by gravity and acceleration caused by the vibration of the accelerometer 253. The approximate shape of the line representing the time-series acceleration detection data D10 is determined by the acceleration caused by gravity. The acceleration caused by the vibration of the accelerometer 253 is manifested as a small vibration on the line.

[0055] exist Figure 7 In the temporal acceleration detection data D10 shown, the detection value is +d in the first attitude and -d in the second attitude. During the movement of diagnostic step 1a from position B to position E, the detection value gradually changes from +d to -d, and at position P1, the detection value reverses from positive to negative. Furthermore, during the movement of diagnostic step 1a from position F to position A, the detection value gradually changes from -d to +d, and at position P2, the detection value reverses from negative to positive. The moment when this detection value reverses can be used as the reference position for generating the magnetic distribution map. For example, if the reversed position P1 is set as the reference position, the cycle from reversed position P1 to the next reversed position P1 is one period.

[0056] The magnetic distribution map generation unit 511 generates a magnetic distribution map that corresponds the timing magnetic detection data D to each position of the escalator 100 based on the moving speed of the diagnostic step 1a and the elapsed time from the reference position P1. That is, the magnetic distribution map M (magnetic detection value, position) for one revolution of the diagnostic step 1a is represented by data sets such as (Dp, P1), ..., (De, E), ..., (Df, F), ..., (Da, A), ..., (Db, B), ..., (Dp, P1), thus allowing it to be represented as a set of (magnetic detection value, position) data. In this way, the second method uses the reversal position P1 appearing in the detection value of the accelerometer 253 as the reference position when generating the magnetic distribution map. As a result, the magnetic distribution map generation operation can be automated and highly accurate.

[0057] Alternatively, the detection value of the sound sensor 252 can be used instead of the detection value of the acceleration sensor 253 to set the reference position of the escalator 100. As long as the location (part) where the maximum sound is detected during one cycle of the diagnostic step 1a is known in advance, the moment when the maximum detection value is detected can be used as the reference position. For example, assuming the diagnostic step 1a is at... Figure 5 At position C, the maximum detection value is the sound of the drive motor 6. In this case, the sound will be detected. Figure 6 The position C of the detected value Dc is set as the reference position. Then, based on the moving speed of the diagnostic step 1a and the elapsed time from the reference position C, a magnetic distribution map M (magnetic detection value, position) is generated. The magnetic distribution map M (magnetic detection value, position) of one loop of the diagnostic step 1a is represented by a set of data such as (Dc, C), ..., (De, E), ..., (Df, F), ..., (Da, A), ..., (Db, B), ..., (Dc, C) (magnetic detection value, position).

[0058] The third method will be explained. In this third method, the position of the diagnostic step 1a when the magnetized component installed in the escalator 100 is detected is used as the reference position. For example, it is assumed that the diagnostic step 1a is in the position of the magnetized component. Figure 5 At position C, the magnetic field of the drive motor 6 is detected as the maximum detection value. In this case, the magnetic field will be detected... Figure 6 The position C of the detected value Dc is set as the reference position. Based on the moving speed of the diagnostic step 1a and the elapsed time from the reference position C, a magnetic distribution map M (magnetic detection value, position) is generated. The magnetic distribution map M (magnetic detection value, position) for one revolution of the diagnostic step 1a is represented by a set of data (Dc, C), ..., (De, E), ..., (Df, F), ..., (Da, A), ..., (Db, B), ..., (Dc, C) as a set of (magnetic detection value, position) data. In this way, by using the position of the diagnostic step 1a at the moment of magnetization of the magnetized component as the reference position, it is not necessary to add a sensor (accelerometer 253) for determining the reference position as in the second method. Therefore, the cost can be reduced.

[0059] (Self-position calculation processing)

[0060] Figure 8 This is a diagram illustrating an example of how one's own position is calculated. Figure 8 In the diagram, the line denoted by the appendix M represents the magnetic distribution map M (magnetic detection value, location), which is related to... Figure 6 The lines shown are of the same shape as the time-series magnetic detection data D. The lines indicated by the reference numeral S are the time-series sound detection data of the sound sensor 252. Figure 8 In the diagram, the vertical axis represents the detection values ​​of the magnetic sensor 251 and the sound sensor 252, and the horizontal axis represents the position within the escalator 100.

[0061] Here, we consider the case where Db is detected as the value of the magnetic sensor 251. When this detection value is fitted to the magnetic distribution map M (magnetic detection value, position), position B is obtained. Figure 8 In the magnetic distribution map M (magnetic detection value, position), there are places where the value Db exists outside of position B. Therefore, when fitting the data, the position of the detection value Db is determined by referring to the detection values ​​before and after Db. That is, it can be known that the position of the diagnostic step 1a when the detection value Db of the magnetic sensor 251 is detected is position B.

[0062] The sound sensor 252's detection value Sb, detected at the same time as the detection value Db, is extrapolated to the sound detection data detected when the diagnostic step 1a is at position B. That is, the self-position extrapolation calculates the position of the diagnostic step 1a within the escalator 100 when the detection value Sb is detected. By performing this self-position extrapolation process, it can be determined where each detection value of the time-series sound detection data S is detected within the escalator 100.

[0063] Figure 9 This is a flowchart illustrating an example of the anomaly detection operation of the anomaly diagnosis device 52. The monitoring center 50 acquires multiple sets of timing detection data from the data collection device 30 by sending a data transmission command to the data collection device 30. Then, anomaly diagnosis processing is performed sequentially on the acquired sets of timing detection data. Figure 9 The flowchart shown illustrates the processing of a single data transmission command, which is repeatedly executed at specified time intervals.

[0064] In step S90, a data transmission command is sent to the data collection device 30 to collect multiple time-series detection data sets accumulated in the data storage unit 301. These time-series detection data sets include various time-series detection data sets corresponding to each sensor installed in the sensor unit 25. Figure 3 In the example shown, because a magnetic sensor 251, a sound sensor 252, and an acceleration sensor 253 are provided in the sensor unit 25, the timing detection data set includes a timing magnetic detection data set, a timing sound detection data set, and a timing acceleration detection data set. In step S91, the first timing detection data set among the multiple timing detection data sets is subjected to noise reduction processing by the noise reduction unit 522.

[0065] In step S92, based on the noise-reduced temporal sound detection data and temporal acceleration detection data, the anomaly estimation unit 521 determines whether an anomaly has occurred. If an anomaly is determined to have occurred (YES), proceed to step S94; otherwise, proceed to step S93. In step S93, based on the noise-reduced temporal sound detection data and temporal acceleration detection data, the anomaly estimation unit 521 determines whether there are any signs of an anomaly. If an anomaly is determined to exist (YES), proceed to step S94; otherwise, proceed to step S97.

[0066] Figure 10This diagram illustrates an example of the anomaly detection judgment in step S92 and the anomaly prediction judgment in step S93. In both the anomaly detection judgment and the anomaly prediction judgment, the noise-reduced timing sound detection data and timing acceleration detection data are compared with judgment reference data to make a judgment. The judgment reference data is stored in the storage unit 523. During the initial operation of generating the magnetic distribution map M (magnetic detection value, position) after setting the diagnostic ladder 1a, a set of timing detection data is acquired from the data collection device 30. The storage unit 523 stores the noise-reduced timing detection data of this set of timing detection data as the judgment reference data. The timing detection data includes timing magnetic detection data, timing sound detection data, and timing acceleration detection data.

[0067] Figure 10 This is a graph representing the temporal sound detection data S11 and S12 obtained through noise reduction processing, and the temporal sound detection data S0 used as the judgment benchmark data. The horizontal axis represents time t. Line S1 is the first judgment line obtained by multiplying each value of the judgment benchmark data S0 by α1. Line S2 is the second judgment line obtained by multiplying each value of the judgment benchmark data S0 by α2 (>α1). The second judgment line S2 is used for anomaly occurrence judgment. The first judgment line S1 is used for anomaly precursor judgment.

[0068] exist Figure 10 In the example shown, the line of the timing sound detection data S11 exceeds the first judgment line S1 near time tc. In this case, the anomaly calculation unit 521 determines that a sign of an anomaly has been observed in a certain component of the escalator 100, i.e., an anomaly is imminent. The timing sound detection data S12 is the line that has been in operation for a further period of time since the timing sound detection data S11 was acquired. The line of the timing sound detection data S12 exceeds the second judgment line S2 near t = tc. In this case, the anomaly calculation unit 521 determines that an anomaly has occurred in a certain component of the escalator 100, i.e., an anomaly has occurred.

[0069] in addition, Figure 10 The method for determining the occurrence or precursor of anomalies based on the time changes of time-series sound detection data has been explained, but the method for determining the occurrence or precursor of anomalies using time-series acceleration detection data is also performed in the same way. If it is determined that at least one of the time-series sound detection data and the time-series acceleration detection data has an anomaly, an anomaly is determined to have occurred in step S92. Similarly, if it is determined that at least one of the time-series sound detection data and the time-series acceleration detection data has a precursor to an anomaly, an anomaly is determined to have a precursor in step S93.

[0070] return Figure 9The flowchart shows that in step S94, the location of the abnormality or the occurrence of abnormal signs is calculated to be at which position of the escalator 100. In the noise reduction process of step S91 above, the time-series magnetic detection data, time-series sound detection data, and time-series acceleration detection data are obtained after noise reduction processing. Figure 10 In the timing sound detection data S11 and S12, the detection value at t=tc is Sc1 in the case of timing sound detection data S11 and Sc2 in the case of timing sound detection data S12. Additionally, the detection value of the timing magnetic detection data at t=tc is as follows... Figure 6 The image shows Dc.

[0071] The anomaly diagnosis device 52 obtains the position of the detected value Dc from the time-series magnetic detection data by performing self-position estimation processing on its own position estimation unit 512. In the self-position estimation processing, the position C (reference value) is obtained by fitting the detected value Dc to the magnetic distribution map M (magnetic detection value, position). Figure 8 As a result, it can be seen that the detection value Sc1, detected at the same time as the detection value Dc, is the detection value obtained when the diagnostic step 1a is located at position C. That is, it can be seen that an anomaly has occurred in the component located near position C. By performing the same processing on the detection value Sc2, it can be seen that an anomaly has occurred in the component located near position C.

[0072] In step S94, the location of any abnormal signs or anomalies is calculated. Then, in step S95, the component experiencing the abnormal signs or anomalies is calculated. For example... Figure 5 As shown, near position C of the escalator 100, a chain 2, a terminal gear 4, a drive gear 5, and a drive motor 6 are installed. In the anomaly calculation unit 521, in addition to calculating the location of the anomaly, it also calculates the location of the anomaly. By calculating not only the occurrence of the anomaly but also the location of the anomaly, a rapid and appropriate response can be taken when an anomaly occurs.

[0073] In the event of an anomaly in chain 2, terminal gear 4, drive gear 5, or drive motor 6, each component emits characteristic abnormal sounds and vibrations. The timing sound detection data and timing acceleration detection data include not only the magnitude of the sound and vibration but also their frequencies. Furthermore, the storage unit 523 pre-stores fault diagnosis data listing the frequencies of abnormal sounds and vibrations for each component and each type of fault. The anomaly estimation unit 521, based on the frequencies of the sound and vibrations at position C, estimates the components and fault details indicating anomalies or signs of anomalies.

[0074] In step S96, an anomaly or anomaly warning is reported by the reporting unit 524. The report information includes at least one of the following: the occurrence of the anomaly or anomaly warning, the location of the anomaly or anomaly warning, the component involved, and the fault details. If the processing in step S96 is complete, the process proceeds to step S97. In step S97, it is determined whether the noise reduction processing and the anomaly occurrence or anomaly warning determination processing have been completed for all multiple timing detection data groups. In step S94, if it is determined that the process has not yet ended (NO), the process returns to step S91; if it is determined that the process has ended (YES), the series of diagnostic processes ends.

[0075] As described above, in the diagnostic system of this embodiment, when a diagnostic step 1a equipped with a diagnostic sensor including a magnetic sensor 251 is installed, the diagnostic step 1a is cyclically moved to generate a magnetic distribution map M (magnetic detection value, position). This magnetic distribution map M (magnetic detection value, position) is a map representing the magnetic state of each position within the escalator 100. Therefore, for the detection position of the diagnostic sensor detection value obtained during escalator operation, the detection position can be obtained by comparing the magnetic detection value detected at the same time as that detection value with the magnetic distribution map M (magnetic detection value, position). That is, even when the operating speed changes, the detection position of the diagnostic sensor detection value can be accurately calculated, and diagnosis can be performed at any time.

[0076] Furthermore, in this embodiment, such as Figure 3 As shown, the data detected by the sensor unit 25 within the diagnostic step 1a is transmitted via communication to a remote monitoring center 50 equipped with a position calculation device 51 and an anomaly diagnosis device 52. Therefore, the status of the escalator 100 can be monitored remotely at any time. Furthermore, multiple escalators can be easily monitored at any time within the monitoring center 50.

[0077] Furthermore, as described above, when calculating the position of step 1a for diagnostic purposes, the detection position is calculated by comparing the detection value of the magnetic sensor 251 with the magnetic distribution map M (magnetic detection value, position). Therefore, for accurate position calculation, it is preferable that the output of the magnetic sensor 251 is stable. Thus, in this embodiment, the offset and drift of the magnetic sensor 251 are corrected by magnetizing the components provided in the escalator 100.

[0078] For example, because the magnetization of the drive motor 6 is stable, the detection value of the magnetic sensor 251 at position C near the drive motor 6 is used as the correction reference value. Using the detection value at position C as the reference means that the detection values ​​at other positions are corrected in a way that ensures the detection value at position C is always constant. For example, if the detection value at position C becomes β times the initial value, correction is performed by multiplying the detection values ​​at all positions by (1 / β). As a result, the influence of output changes of the magnetic sensor 251 can be eliminated, and the deterioration of its own position estimation accuracy can be prevented. This correction operation can be performed by any one of the control unit 26, data collection device 30, position estimation device 51, and anomaly diagnosis device 52 provided in the sensor terminal 24.

[0079] In the above embodiment, based on the detection data of the magnetic sensor 251 and the generated magnetic distribution map M (magnetic detection value, position), a self-position estimation process is performed to correspond the sound detection data with the position in the escalator 100. Furthermore, the self-position estimation process can also be performed simultaneously using the reversal time of the detection data of the accelerometer 253. The reversal time is generated when the diagnostic step 1a moves to a specific position in the escalator 100. Therefore, by utilizing this reversal time, the accuracy of the self-position estimation can be improved, and anomaly diagnosis can be performed with good accuracy. In addition, diagnostic sensors other than the accelerometer 253 can also be used. For example, a height sensor or the like can be added as a diagnostic sensor, and its detection data can be used simultaneously.

[0080] Furthermore, the structure of the diagnostic system 1000 is not limited to Figure 3 The structure is shown in the block diagram.

[0081] (Example 1 of a variation of a diagnostic system structure)

[0082] Figure 11 This is a block diagram illustrating a modified example 1 of the structure of the diagnostic system 1000. Figure 3 The diagnostic system 1000 employs a structure in which multiple sets of time-series detection data stored in the data storage unit 301 are transmitted to the monitoring center 50 via the network 40 through the communication unit 302. On the other hand, in Figure 11 In the diagnostic system 1000A shown, the communication unit 302 of the control device 12 and the communication device 53 of the monitoring center 50 are omitted. The diagnostic system 1000A employs a system where multiple sets of time-series detection data stored in the data storage unit 301 are retrieved by operators during periodic checks and then sent to the monitoring center 50. Data retrieval is performed using portable storage media such as USB flash drives or external hard drives. Because the communication unit 302 and communication device 53 are not required, cost reduction regarding the communication system is achieved.

[0083] (Example 2 of a variation on the structure of a diagnostic system)

[0084] Figure 12 This is a block diagram illustrating a modified example 2 of the structure of the diagnostic system 1000. In the modified example 2 diagnostic system 1000B, the data storage unit 301 and the communication unit 302, previously installed in the control device 12, are installed within the diagnostic ladder 1a. Multiple sets of timing detection data stored in the data storage unit 301 are transmitted from the communication unit 302, installed within the diagnostic ladder 1a, to the communication device 53 of the monitoring center 50 via the network 40.

[0085] (Example 3 of a variation of the diagnostic system structure)

[0086] Figure 13 This is a block diagram representing a modified version 3 of the diagnostic system 1000. The diagnostic system 1000C in modified version 3 omits [details missing]. Figure 12 The diagnostic system 1000B includes a communication unit 302 and a communication device 53. Multiple time-series detection data sets stored in the data storage unit 301 are retrieved by operators during periodic inspections and sent to the monitoring center 50. The position estimation device 51 and the anomaly diagnosis device 52 at the monitoring center 50 generate and diagnose a magnetic distribution map M (magnetic detection value, position) based on the retrieved time-series detection data sets.

[0087] (Example 4 of a variation on the structure of a diagnostic system)

[0088] Figure 14 This is a block diagram representing a modified example 4 of the structure of the diagnostic system 1000. In the diagnostic system 1000D of modified example 4, [the following will be included] Figure 3 The position estimation device 51 and the anomaly diagnosis device 52, which are located in the monitoring center 50 of the diagnostic system 1000, are also located within the control device 12 of the escalator 200. However, the device is located within the control device 12 of the escalator 200. Figure 3 The reporting unit 524 of the abnormal diagnostic device 52 is installed in the monitoring center 50.

[0089] In the diagnostic system 1000D, the generation and storage of the magnetic distribution map M (magnetic detection value, location), and... Figure 9The diagnostic processing shown, excluding the processing in step S96, is performed by the position estimation device 51 and the anomaly diagnosis device 52 installed in the control device 12. If the diagnostic processing results in an anomaly or an anomaly precursor, a report is sent from the communication unit 302 to the communication device 53 of the monitoring center 50 via the network 40. The report is displayed by the reporting unit 524. Alternatively, the monitoring center 50 can also acquire the timing detection data set stored in the data storage unit 301 of the data collection device 30 by sending a timing detection data set transmission command to the control device 12.

[0090] (Example 5 of a variation on the structure of a diagnostic system)

[0091] Figure 15 This is a block diagram representing a modified example 5 of the structure of the diagnostic system 1000. The diagnostic system 1000E in modified example 5... Figure 14 In the diagnostic system 1000D shown, the communication unit 302 of the data collection device 30 is replaced with a wireless communication unit 302A for short-range communication. The communication device 53 of the monitoring center 50 is omitted, and an information processing device 54, such as a personal computer, is installed instead of the reporting unit 524 of the monitoring center 50. The wireless communication unit 302A receives detection data from sensors 251 to 253 from the wireless communication unit 27 installed in the diagnostic ladder 1a.

[0092] The results of the diagnostic processing by the position estimation device 51 and the anomaly diagnosis device 52 installed in the control device 12, the generated magnetic distribution map M (magnetic detection value, position), and the time-series detection data set stored in the data storage unit 301 are retrieved by operators during periodic inspections and sent to the monitoring center 50. At the monitoring center 50, the diagnostic processing results and the magnetic distribution map M (magnetic detection value, position) can be confirmed via the information processing device 54. Furthermore, the information processing device 54 can also be used to confirm and analyze the retrieved time-series detection data set.

[0093] In addition, Figure 15 In the illustrated structure, the anomaly diagnostic device 52 does not include a reporting unit 524, but it is also possible to include a reporting unit 524 in the anomaly diagnostic device 52. By including the reporting unit 524, operators can identify abnormal conditions during periodic inspections and respond to them on the spot. Of course, even in a structure without the reporting unit 524, the diagnostic results can be confirmed by reading the diagnostic results stored in the storage unit 523 using a personal computer or similar device.

[0094] (Example 6 of a variation on the structure of a diagnostic system)

[0095] Figure 16This is a block diagram illustrating a modified version 6 of the structure of the diagnostic system 1000. In this modified version 6, the diagnostic system 1000F is configured within the diagnostic ladder 1a. Figure 14 The diagnostic system 1000D includes a data storage unit 301, a communication unit 302, a position calculation device 51, and an anomaly diagnosis device 52 in the control device 12, but omits the wireless communication unit 27 installed in the diagnostic ladder 1a.

[0096] In the diagnostic system 1000F, the storage of detection data detected by the sensor unit 25, the generation of the magnetic distribution map M (magnetic detection value, position) based on the detection data, and the diagnostic processing based on the detection data and the magnetic distribution map M (magnetic detection value, position) are all performed by the data storage unit 301, the position estimation device 51, and the anomaly diagnosis device 52 installed in the diagnostic ladder 1a. The diagnostic results are sent from the communication unit 302 to the communication device 53 of the monitoring center 50 via the network 40. In the monitoring center 50, a report is generated by the reporting unit 524 based on the received diagnostic results. Of course, the monitoring center 50 can also obtain the timing detection data group stored in the data storage unit 301 by sending a transmission command for the timing detection data group to the control device 12.

[0097] (Example 7 of a variation on the structure of a diagnostic system)

[0098] Figure 17 This is a block diagram representing a modified version 7 of the diagnostic system 1000. The diagnostic system 1000G in modified version 7... Figure 16 In the diagnostic system 1000F shown, the communication unit 302 installed in the diagnostic ladder 1a is omitted, the communication device 53 of the monitoring center 50 is omitted, and the reporting unit 524 of the monitoring center 50 is replaced by an information processing device 54 such as a personal computer.

[0099] The diagnostic results of the position estimation device 51 and the anomaly diagnosis device 52 installed in the diagnostic ladder 1a, the generated magnetic distribution map M (magnetic detection value, position), and the time-series detection data set stored in the data storage unit 301 are retrieved by the operators during periodic inspections and sent to the monitoring center 50. At the monitoring center 50, the diagnostic results and the magnetic distribution map M (magnetic detection value, position) can be confirmed via the information processing device 54. Furthermore, the information processing device 54 can also be used to confirm and analyze the retrieved time-series detection data set. Similarly to Modification 5, during periodic inspections, operators can read the diagnostic results stored in the storage unit 523 using a personal computer or similar device to confirm the diagnostic results and address anomalies on the spot. Additionally, a reporting unit 524 can also be installed.

[0100] In addition, Figure 3 , 11In the block diagrams shown in ~17, the functional units in the structure, such as the position calculation device 51 and the anomaly diagnosis device 52, can be implemented, in addition to circuits, electronic circuits, logic circuits, and integrated circuits built into them, by programs executed by combinations of microcomputers, processors and similar arithmetic devices, ROMs, RAMs, flash memory, hard disks, SSDs, memory cards, optical disks and similar storage devices, buses, networks and similar communication devices, and peripheral devices. The present invention can be established under any implementation. Furthermore, in the embodiments, two or more programs can be implemented as one program, and one program can be implemented as two or more programs.

[0101] Based on the embodiments of the present invention described above, the following effects can be obtained.

[0102] (C1) such as Figure 1 , 3 As shown, the diagnostic system 1000 is a diagnostic system for diagnosing malfunctions in an escalator 100, which is a passenger conveyor that transports passengers by cyclically moving a plurality of steps 1, 1a connected in a loop. The diagnostic system 1000 includes: a diagnostic step 1a, which includes at least one diagnostic sensor, including a magnetic sensor 251, among the plurality of steps 1, 1a; and a magnetic distribution map generation unit 511, which is used to generate a magnetic distribution map M (magnetic detection value, position). The magnetic distribution map M (magnetic detection value, position) is the correlation between the position of the escalator 100 during one cycle of the diagnostic step 1a and the detection value of the magnetic sensor 251; the self-position estimation unit 512 estimates the position of the detection value of the sound sensor 252 of the escalator 100 based on the magnetic distribution map M (magnetic detection value, position) and the detection value of the magnetic sensor 251 during cyclic movement; and the anomaly estimation unit 521 performs anomaly diagnosis based on the detection value of the sound sensor 252.

[0103] like Figure 8 As shown, the magnetic distribution map M (magnetic detection value, position) represents the correlation between the position of the escalator 100 during one cycle of the diagnostic step 1a and the detection value of the magnetic sensor 251. Based on this magnetic distribution map M (magnetic detection value, position) and the detection value of the magnetic sensor 251 during cyclic movement, the position of the detection value of the sound sensor 252 in the escalator 100 can be estimated. Therefore, even when the operating speed of the escalator changes during operation, the detection position of the diagnostic sensor can be estimated with good accuracy, enabling real-time diagnosis.

[0104] (C2) such as Figure 5 , 7As shown, the diagnostic step 1a can be configured such that it includes an accelerometer 253 and a sound sensor 252, which are used as position determination sensors to determine the position of the cyclically moving diagnostic step 1a in the escalator 100. The magnetic distribution map generation unit 511 uses the position determined by the position determination sensor (e.g., the reverse position P1) as a reference position to generate a magnetic distribution map M (magnetic detection value, position). By using the position determined by the position determination sensor as the reference position for generating the magnetic distribution map, automation and high precision of the magnetic distribution map generation operation can be achieved.

[0105] (C3) Alternatively, the diagnostic ladder 1a may include an accelerometer 253 as a position determination sensor; the magnetic distribution map generation unit 511 calculates the moment when the attitude of the diagnostic ladder 1a reverses based on the sensor output of the accelerometer 253, and uses the reversed position P1 of the diagnostic ladder 1a at that moment as a reference position to generate a magnetic distribution map M (magnetic detection value, position). The accelerometer 253 can easily and accurately detect the reversed position P1.

[0106] (C4) such as Figure 5 , 6 As shown, the escalator 100 can also be configured such that it includes a magnetized component (e.g., the drive motor 6), and the magnetic distribution map generation unit 511 uses the position C of the step 1a as a reference position for the diagnostic method when the magnetic sensor 251 detects the magnetization of the magnetized component, and generates a magnetic distribution map M (magnetic detection value, position). By using the position of the step 1a as the reference position for the diagnostic method when the magnetized component is detected, it is not necessary to additionally install a sensor (accelerometer 253) for determining the reference position. Therefore, cost reduction can be achieved.

[0107] (C5) such as Figure 5 , 7 As shown, the self-position estimation unit 512 can also be configured such that, based on the magnetic distribution map M (magnetic detection value, position) and the detection values ​​(e.g., reverse position P1) of the magnetic sensor 251 and the accelerometer 253 used as a position determination sensor during cyclic movement, it estimates the position detected by the sound sensor 252 used as a diagnostic sensor. The reverse position P1 detected by the accelerometer 253 is generated when the diagnostic step 1a moves to a specific position on the escalator 100. Therefore, by also using this reverse position P1 for self-position estimation, the accuracy of self-position estimation can be improved, and abnormality diagnosis can be performed with good accuracy.

[0108] (C6) Furthermore, the escalator 100 may be configured to include a magnetized component (e.g., the drive motor 6), and the output error of the magnetic sensor 251 may be corrected based on the magnetization of the magnetized component. For example, correction processing may be performed in the control unit 26 or the data collection device 30. As a result, the influence of output changes in the magnetic sensor 251 can be eliminated, and the deterioration of its own position calculation accuracy can be prevented.

[0109] (C7) such as Figure 9 As in step S95, the anomaly estimation unit 521 estimates the abnormal component based on the position estimated by its own position estimation unit 512 and the detection data from the diagnostic sensor (e.g., the sound sensor 252). By estimating not only the occurrence of an anomaly but also the abnormal component, a rapid and appropriate response can be provided when an anomaly occurs.

[0110] (C8) such as Figure 3 , 12 As shown, the diagnostic system also includes: a communication unit 302, which is installed in the frame 3 of the escalator 100, which is equipped with a drive motor 6 that cyclically moves multiple steps 1, 1a, or a diagnostic step 1a; and a monitoring center 50, which communicates with the communication unit 302 to send and receive data. As a remote monitoring unit equipped with a self-position calculation unit 512 and an anomaly diagnostic device 52, it sends the detection data of the diagnostic sensors to the monitoring center 50 via the communication unit 302. Thus, the escalator 100 can be remotely monitored at any time.

[0111] (C9) Furthermore, such as Figure 3 As shown, it can also be configured such that the communication unit 302 is installed in the frame 3 of the escalator 100, and the diagnostic step 1a also includes a wireless communication unit 27. This wireless communication unit 27 transmits the detection data of the diagnostic sensor to the communication unit 302 installed in the frame 3 via wireless communication. The communication unit 302 transmits the detection data from the diagnostic step 1a to the monitoring center 50. The wireless communication unit 27 can use a low-power, short-range wireless device, and can use a small-capacity power source such as a battery as the power source for the diagnostic step 1a.

[0112] (C10) such as Figure 14 , 16 As shown, the self-position calculation unit 512 and the anomaly calculation unit 521 are installed in the frame 3 of the escalator 100, which is equipped with a drive motor 6 that causes multiple steps 1, 1a to move cyclically, or in the diagnostic step 1a. The escalator 100 also includes: a communication unit 302, which is installed in the frame 3 or in the diagnostic step 1a; and a monitoring center 50, which communicates with the communication unit 302 to send and receive data, and sends the diagnostic results of the anomaly calculation unit 521 to the monitoring center 50 via the communication unit 302.

[0113] With this structure, the monitoring center 50 only needs to be equipped with a device capable of receiving inspection results, such as a personal computer or mobile phone, to receive diagnostic results via the Internet. Therefore, even without setting up a large-scale monitoring center, it is possible for operators to receive diagnostic results using information terminals and inspect escalators 100 that exhibit abnormalities.

[0114] (C11) such as Figure 11 , 13 As shown in Figures 15 and 17, the escalator 100, equipped with a drive motor 6 that cyclically moves multiple steps 1 and 1a, includes a housing frame 3 or diagnostic steps 1a, and a data storage unit 301 for storing detection data from the sensor unit 25. In this case, the detection data accumulated in the data storage unit 301 is retrieved by the operator, and the data analysis of the detection data is performed by the self-position estimation unit 512 and the anomaly estimation unit 521, eliminating the need for a communication device.

[0115] (C12) such as Figure 15 , 17 As shown, the self-position calculation unit 512 and the anomaly calculation unit 521 are installed in the frame 3 or diagnostic step 1a of the escalator 100, which is equipped with a drive motor 6 that cyclically moves multiple steps 1, 1a. The frame 3 or diagnostic step 1a also includes a storage unit 523 for storing the diagnostic results of the anomaly calculation unit 521. In this case, during periodic inspections, operators can confirm the diagnostic results by reading them from the storage unit 523 using a personal computer or similar device, and can respond to anomalies on the spot.

[0116] The various embodiments and modifications described above are merely examples, and the present invention is not limited to these contents as long as they do not impair the features of the invention. For example, the present invention can also be applied to passenger conveyors other than escalators. Furthermore, while various embodiments and modifications have been described above, the present invention is not limited to these contents. Other methods conceivable within the scope of the technical concept of the present invention are also included within the scope of the present invention.

[0117] Explanation of reference numerals in the attached figures

[0118] 1…step, 1a…diagnostic step, 2…chain, 3…box frame, 4…terminal gear, 6…drive motor, 9…lower terminal gear, 12…control device, 24…sensor terminal, 25…sensor unit, 26…control unit, 27…wireless communication unit, 30…data collection device, 50…monitoring center, 51…position estimation device, 52…anomaly diagnosis device, 53…communication device, 54…information processing device, 251…magnetic sensor, 252…sound sensor, 253…accelerometer, 301…data storage unit, 302…communication unit, 511…magnetic distribution map generation unit, 512…self-position estimation unit, 513, 522…noise reduction unit, 514, 523…storage unit, 521…anomaly estimation unit, 524…reporting unit, 1000, 1000A~1000G…diagnostic system.

Claims

1. A diagnostic system for diagnosing anomalies in a passenger conveyor that transports passengers by cyclically moving a plurality of steps connected in a loop, said diagnostic system being characterized by comprising: A diagnostic ladder, comprising, among the plurality of ladders, having at least one diagnostic sensor including a magnetic sensor; A magnetic distribution map generation unit is used to generate a magnetic distribution map that represents the correlation between the position in the passenger conveyor and the detection value of the magnetic sensor during one cycle of the diagnostic ladder. A position estimation unit, based on the magnetic distribution map and the detection values ​​of the magnetic sensors during cyclic movement, estimates the position in the passenger conveyor where the detection values ​​of the diagnostic sensors were detected; and The anomaly diagnosis unit performs anomaly diagnosis based on the detection values ​​of the diagnostic sensors. The diagnostic sensors include a sound sensor. When the detection value of the magnetic sensor is fitted to the magnetic distribution map, a specified position is obtained. The sound detection value of the sound sensor, detected at the same time as the detection value of the magnetic sensor, is extrapolated to the sound detection data detected when the diagnostic ladder is in the specified position.

2. The diagnostic system as described in claim 1, characterized in that: The diagnostic ladder, serving as the diagnostic sensor, includes a position determination sensor capable of determining the position of the cyclically moving diagnostic ladder within the passenger conveyor. The magnetic distribution map generation unit uses the position determined by the position determination sensor as a reference position to generate the magnetic distribution map.

3. The diagnostic system as described in claim 2, characterized in that: The diagnostic ladder includes an accelerometer as the position determination sensor. The magnetic distribution map generation unit calculates the moment of attitude reversal of the diagnostic ladder based on the sensor output of the accelerometer, and uses the position of the diagnostic ladder at that moment as a reference position to generate the magnetic distribution map.

4. The diagnostic system as described in claim 1, characterized in that: The passenger conveyor includes magnetized components. The magnetic distribution map generation unit uses the position of the diagnostic ladder when the magnetic sensor detects the magnetization of the magnetized component as the reference position to generate the magnetic distribution map.

5. The diagnostic system as described in claim 2, characterized in that: The position estimation unit calculates the position at which the detection value of the diagnostic sensor is detected based on the magnetic distribution map and the detection values ​​of the magnetic sensor and the position determination sensor during cyclic movement.

6. The diagnostic system as described in claim 1, characterized in that: The passenger conveyor is equipped with magnetized components. The diagnostic system also includes a correction unit that corrects the output error of the magnetic sensor based on the magnetization of the magnetizing component.

7. The diagnostic system as described in claim 1, characterized in that: The anomaly diagnosis unit calculates the abnormal component based on the position calculated by the position estimation unit and the detection data of the diagnostic sensor.

8. The diagnostic system as described in claim 1, characterized in that, Also includes: A communication device is provided at a fixed part of the passenger conveyor or at the diagnostic step, which is equipped with a drive device that causes a plurality of the steps to move cyclically. and The remote monitoring unit communicates with the communication device to send and receive data, and includes the location calculation unit and the anomaly diagnosis unit. The detection data from the diagnostic sensor is transmitted to the remote monitoring unit via the communication device.

9. The diagnostic system as described in claim 8, characterized in that: The communication device is installed at the fixed part of the passenger conveyor. The diagnostic ladder also includes a wireless communication device, which transmits the detection data from the diagnostic sensor to the communication device disposed on the fixed part via wireless communication. The communication device will send the detection data from the diagnostic ladder to the remote monitoring unit.

10. The diagnostic system as claimed in claim 1, characterized in that: The position calculation unit and the anomaly diagnosis unit are installed in the fixed part of the passenger conveyor or the diagnostic steps equipped with a drive device, the drive device causing the plurality of steps to move cyclically. The diagnostic system also includes: A communication device, disposed at the fixed part of the passenger conveyor or the diagnostic step; and The remote monitoring unit communicates with the communication device to send and receive data. The diagnostic results of the anomaly diagnosis unit are sent to the remote monitoring unit via the communication device.

11. The diagnostic system as claimed in claim 1, characterized in that: The passenger conveyor's fixing part or the diagnostic step, which is equipped with a drive device that causes the plurality of steps to move cyclically, further includes a data storage unit for storing the detection data of the diagnostic sensor.

12. The diagnostic system as described in claim 1, characterized in that: The position calculation unit and the anomaly diagnosis unit are installed in the fixed part of the passenger conveyor or the diagnostic steps equipped with a drive device, the drive device causing the plurality of steps to move cyclically. The fixing part or the diagnostic ladder, which is provided with the position calculation part and the anomaly diagnosis part, also includes a diagnosis result storage part for storing the diagnosis results of the anomaly diagnosis part.

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