Machine behavior visualization device

Abstract

To grasp an overview of physical behavior of utilization members, consumable members and the like in a machine constitution, without looking at the actual members individually.SOLUTION: A machine behavior visualization device 1 comprises: sensors which are provided at each part of a machine, and are adapted to detect behavior of each part; an image data processing part 1A for converting signals respectively detected by each sensor, to image information as visual information according to an intensity of each signal in a fixed time unit; a mapping processing part 1B using the horizontal axis as the time axis, and mapping the image information that has been converted for each sensor on the vertical axis; display means 1E for displaying an image output generated by the mapping processing part 1B; and adjustment means (an adjustment part 1C) for re-mapping the image information in accordance with time axis unit adjustment in a display area of the display means 1E.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a device that can grasp the behavior of production machines, manufacturing machines, etc. in real time without seeing the actual machines or products. [Background technology]

[0002] Forging is highly productive and suitable for mass production, but on the other hand, if there is an abnormality in the die, a large number of defective products will be produced. For this reason, it is important to understand the condition of the die in order to continue stable production.

[0003] Sensor technology is used to understand the operation, i.e., behavior, including abnormalities, of production machinery, manufacturing machinery, etc. (hereinafter collectively referred to as "machines" in this application). Sensors are adopted that are appropriate for what needs to be detected. For example, Patent Documents 1 to 4 listed below describe techniques for detecting and predicting abnormalities using AE sensors (Acoustic Emission Sensors: acoustic reflection detection sensors).

[0004] Patent Document 1 (Japanese Patent Application Laid-Open No. 150090 / 1977) proposes a configuration in which, when examining the fatigue strength of materials in material mechanics, in order to accurately grasp the timing of crack initiation, its progression and behavior, etc., multiple sets of preamplifiers and AE sensors are connected to an AE positioning monitoring device with a zone selection function, one end of a pinpoint contact type waveguide is connected to each of the AE sensors, the waveguides are supported by a waveguide fixture made of acoustic insulating material, and the other end of the waveguide is acoustically connected to the specimen.

[0005] Furthermore, Patent Document 2 (JP Patent Publication No. 4-310857) proposes that in order to monitor the progression of minute cracks in bridge structures that do not affect their strength, multiple AE sensors are arranged to form spatial filters near the crack tip position in the bridge structural member and in the predicted crack progression direction, thereby creating limited monitoring areas, and extracting only AE signals from vibrations caused by running wheels that originate within each monitoring area, and estimating the progression of the crack from the changes in these signals over time.

[0006] Furthermore, Patent Document 3 (Japanese Patent Laid-Open Publication No. 8-159151) proposes that in order to accurately estimate the remaining life of a rolling bearing, vibration information, temperature information, and load information of the rolling bearing unit while it is in operation are monitored simultaneously, and the results are compared with fluctuations in vibration information and temperature information associated with bearing damage corresponding to each load condition measured in advance, thereby predicting the degree of bearing damage and remaining life.

[0007] Furthermore, Patent Document 4 (JP 2004-170397 A) proposes a system in which one AE sensor is attached to each measurement location, corresponding to each predicted location of crack initiation or propagation in the structure, in order to detect and monitor the level of damage to the entire structure; the AE sensor performs primary processing of the AE signal detected by the composite probe, stores the primary processed data, and communicates it to the outside; the AE sensor is connected to a data processing unit that requests and receives output of the primary processed data from the AE sensor and performs secondary processing of the received primary processed data; and the safety of the structure is evaluated based on the secondary processed data; the primary processing of the AE signal in the AE sensor involves counting the number of times a threshold value is exceeded per unit time that is input in advance, and creating primary processed data that indicates an evaluation rank according to this count; and the secondary processing in the data processing unit involves noise removal by comparing the AE signal with supplementary data that is stored in the data processing unit in advance or transmitted from an external source.

[0008] However, Patent Documents 1 to 4 are methods of observing values ​​and waveform changes caused by unusual machine behavior in data collected in a time series, but do not go so far as to grasp the machine behavior from a bird's-eye view. "A bird's-eye view" basically means that the physical phenomena accompanying the operation of moving parts, consumable parts, etc. in the machine configuration, that is, the behavior, can be grasped, for example, in the limited display area of ​​a single screen, without having to look at the actual parts individually. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Japanese Patent Publication No. 52-150090 [Patent Document 2] Japanese Patent Application Publication No. 4-310857 [Patent Document 3] Japanese Patent Application Publication No. 8-159151 [Patent Document 4] Japanese Patent Application Laid-Open No. 2004-170397 Summary of the Invention [Problem to be solved by the invention]

[0010] The problem that the present invention aims to solve is that it is not possible to grasp the physical behavior of moving parts, consumable parts, etc. in the configuration of a machine from a bird's-eye view without looking at each of the actual parts. [Means for solving the problem]

[0011] In order to solve the above problems, the machine behavior visualization device of the present invention comprises sensors provided in each part of the machine and suitable for detecting the behavior of each part; an image data processing unit that converts the signals detected by each of the sensors into image information as visual information corresponding to the strength of each signal in fixed time units; a mapping processing unit that maps each of the image information converted for each of the sensors on the vertical axis, with the horizontal axis as the time axis; display means that displays the image output generated by the mapping processing unit; and adjustment means that remaps the image information in accordance with adjustment of the time axis unit in the display area of ​​the display means. [Effects of the Invention]

[0012] The present invention allows the outputs of multiple sensors installed in various parts of a machine to be grasped on the same time axis within the display area. Therefore, for example, it is possible to grasp which part is experiencing an abnormality and at what timing without having to check each part of the machine or the defective part of the product, or even before a product defect occurs. Furthermore, the present invention allows the time axis within the display area to be freely adjusted to either the long term or the short term, so it is possible to see long-term operating trends or pinpoint short-term operating conditions. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1A is a block diagram showing the configuration of a machine behavior visualization device of the present invention, and FIG. 1B is a diagram showing the configuration and sensor configuration of a machine behavior visualization device (machine side) of the present invention. [Figure 2] 10(a) to 10(c) are diagrams showing data conversion statuses of an image data processing unit. [Figure 3] FIG. 1 is a diagram for explaining a strength Gantt chart by the machine behavior visualization device of the present invention. [Figure 4] 1 is a diagram for explaining a color graph produced by the machine behavior visualization device of the present invention. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0014] In order to grasp the behavior of machine operation without seeing the actual machine configuration or the products manufactured by the machine, the present invention comprises sensors that are provided in each part of the machine and are suitable for detecting the behavior of each part; an image data processing unit that converts the signals detected by each of the sensors into image information as visual information corresponding to the strength of each signal in fixed time units; a mapping processing unit that maps each of the image information converted for each of the sensors on the vertical axis, with the horizontal axis as the time axis; display means that displays the image output generated by the mapping processing unit; and adjustment means that remaps the image information in accordance with adjustment of the time axis unit in the display area of ​​the display means.

[0015] In this example, the machine behavior visualization device of the present invention is implemented in a cold forging machine. To implement the present invention, the machine behavior visualization device 1 (hereinafter referred to as visualization device 1) of the present invention mainly comprises, as shown in Fig. 1(a), an AE sensor S1, a load sensor S2, and a displacement sensor S3 (these are representative examples) provided in a part of the cold forging machine shown in Fig. 1(b), and an image data processing unit 1A, a mapping processing unit 1B, an adjustment unit 1C, a storage unit 1D, and a display unit 1E arranged in a location separate from the cold forging machine.

[0016] As will be described later, there are many other sensors in addition to those mentioned above, but for the sake of explanation, only representative sensors S1 to S3 will be shown. The visualization device 1 includes a display means 1E that displays an image output generated by the mapping processing unit 1B, which also serves as a notification means that issues an alarm sound, for example, to indicate that an abnormality has been predicted, and a display means as a so-called monitor that displays an alarm and various other displays.The visualization device 1 also includes a hardware configuration that is provided in a general personal computer, such as an input means for inputting commands, etc., a storage means that stores a processing program as software, a RAM, a CPU, etc.

[0017] Furthermore, the configuration of the various sensors and cold forging device including sensors S1 to S3 in the visualization device 1 shown in Figure 1(b) and the configuration other than the sensors in the visualization device 1 including the mapping processing unit 1B, adjustment unit 1C, memory unit 1D, and display means 1E may be configured remotely from each other as long as signals can be sent and received.

[0018] Next, the configuration of the machine side, i.e., the cold forging device in this example, will be described. The cold forging device has the following basic components: a slide 2, a bolster 3, an upper die 4, a lower die 5, a die height 6, an inner punch 7, a punch 8, a die 9 (metal mold), a die cushion 10, a cushion plate 11, and a knockout 12.

[0019] Furthermore, in the above-mentioned cold forging device, the visualization device 1 is configured with the above-mentioned sensors S1 to S3 at various parts of the machine. The AE sensor S1 was provided on the cushion plate 11 so that it could detect acoustic reflection from the mold during manufacturing. The load sensor S2 was installed at a location where it could measure the load applied to the main parts such as the die 9, knockout 12, and inner punch 7 during molding. The displacement sensor S3 was installed at a location where it could measure the press stroke and the gap between the dies in order to clarify the positional relationship of the dies.

[0020] In addition, in addition to the above-mentioned sensors S1 to S3, the visualization device 1 of this example includes many other possible sensors shown in Figure 1(a), such as various sensors such as temperature, sound, vibration (acceleration), and strain sensors, as well as control signals for equipment such as presses and peripheral devices, actuator current, hydraulic and pneumatic pressure, and proximity sensors, but to avoid complicating the explanation, we have limited ourselves to providing examples of the above-mentioned sensors S1 to S3.

[0021] In the cold forging device shown in Figure 1(b), material is formed as follows. Before forming, the cushion plate 11 to which the die 9 is attached is positioned above the lower die 5 by the die cushion 10. At the start of forming, the punch 8 and die 9 come into contact as the slide 2 descends. After that, the punch 8 pushes the die 9, causing the cushion plate 11 to descend, and the material is pushed into the forming portion P by the fixed knockout 12, completing the forming process at the bottom dead center.

[0022] Next, the configuration of the visualization device 1 of this example other than the machine side will be described. The image data processing unit 1A converts the detection signals from the sensors S1 to S3 into image information as visual information according to the strength of the detection signals. Here, the image information means the converted color, for example, dark (strong) - light (weak), according to the strength of the detection signals.

[0023] The mapping processing unit 1B synchronizes and maps the image information from each of the sensors S1 to S3 on the vertical axis and the horizontal axis as the time axis. In the present invention, the image information from each of the sensors is arranged on the vertical axis and the horizontal axis is used as the time axis, so the strength of the detection signals from "all sensors" (including sensors not illustrated in this example in addition to each of the sensors S1 to S3) cannot be expressed on the vertical and horizontal axes, but by using image information as described above, and in this example, the strength of the detection signals is expressed by shades of color in addition to the vertical and horizontal axes, all sensors can be aligned on the vertical axis in synchronization with time on the horizontal axis, and can be viewed as a Gantt chart.

[0024] Here, we will explain the process of "converting the sensor detection signals into image information and performing mapping processing" in the image data processing unit 1A and mapping processing unit. In Figure 2, we will explain how the AE sensor S1, which represents each sensor, converts the detection signals of one shot of the AE sensor S1 collected during press forming into image information.

[0025] The image data processing unit 1A obtains an AE detection signal for one shot of AE waves (detection signal) as shown in Figure 2(a), which is a time-series graph with time on the horizontal axis and effective value on the vertical axis. Next, in this example, the image data processing unit 1A converts the signal into a graph with a logarithmic scale on the vertical axis, as shown in Figure 2(b), to make it easier to capture changes in the AE waves. Note that the logarithmic conversion in Figure 2(b) is not essential.

[0026] The image data processing unit 1A converts one shot of AE waves shown in Fig. 2(b) into one color bar (image information). The color bar shows the vertical axis, i.e., the effective value of the AE waves, shown in Fig. 2(c) by using shades of color. Note that although the illustration shows shades of black and white, the actual color is used.

[0027] The image data processing unit 1A outputs the image information to the mapping processing unit 1B and the storage unit 1D. The color bars are output to the mapping processing unit 1B in chronological order (with the horizontal axis as the time axis). As shown in FIG. 3, the mapping processing unit 1B maps the color bars of all sensors in synchronization with time on the vertical axis, with the horizontal axis as the time axis. The mapping image information is output as a mapping image to the storage unit 1D and the display means 1E via the adjustment unit 1C. Hereinafter, this mapping image information will be referred to as a "strength / weakness Gantt chart." This allows the user to see almost all behavioral changes in the cold forging device configuration, along with the signal strength as well as the timing.

[0028] On the other hand, the mapping processing unit 1B also performs the mapping processing shown in FIG. 4 in parallel, in addition to the above mapping processing, so that it can check changes in individual sensors, for example.

[0029] Continuing to explain using AE sensor S1 as a representative of all sensors, mapping processing unit 1B further fixes the horizontal axis of the color bar shown in FIG. 2(b) above to the time of just one shot, the vertical axis to the number of shots, and expresses the strength of the AE sensor detection data in color (such as shades of color). In other words, the detection signal of AE sensor S1 during a molding operation in which one shot is repeated is converted into a color bar, and the color bar is cumulatively mapped as shown in FIG. 4. This mapping image information is hereinafter referred to as a "color graph." The color graph is output as needed to memory unit 1D, and is cumulatively stored there.

[0030] In this example, the horizontal axis is fixed to the time of one shot because the explanation is for the AE sensor S1 attached to a component that performs cyclical operation, but the horizontal axis can be in units of, for example, one day, one hour, one minute, or ten seconds, or any other length. In short, this color graph is suitable for understanding the behavioral changes of "only" one sensor.

[0031] The memory unit 1D stores all processing results for all sensors output from the mapping processing unit 1B. The display means 1E initially and permanently displays the intensity Gantt chart. The adjustment unit 1C reads information from the memory unit 1D in response to operations by the operator, such as adjusting the time axis unit that serves as the horizontal axis in the intensity Gantt chart, displaying only selected sensors, or displaying the color graph for the selected sensors, and performs adjustment processing in accordance with the operation to switch from the initial display to the display.

[0032] By lengthening the time axis using the adjustment unit 1C, it becomes possible to view all sensor outputs from a more bird's-eye view. For example, when an operation is performed to adjust the time axis that serves as the horizontal axis of the intensity Gantt chart, for example, to display information from several hours ago stored in the memory unit 1D in hourly units, the adjustment unit 1C reads out the intensity Gantt chart for that earlier time from the memory unit 1D, and outputs the intensity Gantt chart, which has been remapped onto the time axis, as a color bar of the strongest signal (or the weakest *depending on the detection characteristics of the sensor) within a one-hour range for each of the sensors, to the display means 1E.

[0033] Furthermore, for example, when an operation is performed to change the horizontal time axis of the strength Gantt chart, which is currently displayed in hourly units as described above, to display it in minutes, the strength Gantt chart is remapped onto the time axis as a color bar of the strongest signal (or the weakest *depending on the detection characteristics of the sensor) within a one-minute range for each of all sensors, and is output to the display means 1E.

[0034] In addition, the display of the color bar by changing the unit (one division on the horizontal axis) time range may be performed by calculating the average value of the strength within the time range as described above, and then outputting a strength Gantt chart that has been remapped onto the time axis as a color bar of this average value signal to the display means 1E.

[0035] Furthermore, for example, when an operation is performed to display only the selected sensor in the strength Gantt chart, the color bars from sensors other than the selected sensor will no longer be displayed, and the color bar width in the vertical axis direction will be expanded accordingly, and output to the display means 1E.

[0036] Furthermore, when an operation is performed to display a color graph for a selected sensor, the color graph for that sensor is read out from the storage unit 1D and displayed on the display means 1E.

[0037] With the present invention, as a display method, in Gantt charts and timing charts, many sensors can be lined up on the vertical axis, but the signal strength is not shown there. However, by converting the signal strength into image information as visual information such as color shading, it becomes possible to visualize the behavior of machine operation without having to look at the configuration or the product. Since the signal strength including the timing of all sensors installed in the component parts throughout the fine details of the machine configuration can be seen at a glance, the behavior of machine operation can be grasped from a bird's eye view without looking at the actual machine configuration or the product made by that machine. [Explanation of symbols]

[0038] 1. (Machine behavior) visualization device 1A Image data processing unit 1B Mapping processing section 1C Adjustment unit (remapping adjustment means) 1E Display means

Claims

[Claim 1] A machine behavior visualization device for understanding the behavior of machine operation without looking at the actual machine configuration or the products made by that machine, the machine behavior visualization device comprising: sensors provided in each part of the machine and suitable for detecting the behavior of each part; an image data processing unit that converts the signals detected by each of the sensors into image information as visual information corresponding to the strength of each signal in fixed time units; a mapping processing unit that maps each of the image information converted for each of the sensors on the vertical axis, with the horizontal axis as the time axis; a display means that displays the image output generated by the mapping processing unit; and an adjustment means that remaps the image information in accordance with adjustment of the time axis unit in the display area of ​​the display means.

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