Robotic Signature Analysis (RSA)

Abstract

A method comprising: receiving a first waveform based on robot sensing using a wireless sensor; determining whether a first value of a first peak of the first waveform exceeds a first threshold; determining whether the first waveform represents a first vibration signature; and performing a predictive maintenance analysis of the robot if the first value exceeds a first threshold or if the first waveform represents a first vibration signature, wherein the first waveform represents a first acceleration during a first period.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application claims priority to U.S. Provisional Patent Application No. 63 / 504,066, filed on May 24, 2023, which is incorporated herein by reference in its entirety.

Background Art

[0002] Robots provide reliable, reproducible, accurate, and cost - effective production of articles in the automotive, pharmaceutical, and other industries. Robots can perform tasks that are too dangerous or too repetitive for human operators. Such tasks include welding, part handling, sealing, and painting in the automotive industry. For these reasons, robots continue to gain popularity in manufacturing lines.

[0003] Manufacturers often operate manufacturing lines along strict schedules that allocate time frames for robot training, production, maintenance, and downtime. The functionality and accuracy of robots can degrade over time, and even with preventive maintenance programs in place, robots can fail completely. When the functionality and accuracy of a robot degrade, the robot can produce low - quality parts or further damage parts. When a robot fails, the manufacturing line stops, and the manufacturer may not be able to fully utilize the time frame for robot production.

[0004] Robot degradation and malfunctions result in increased maintenance costs, as well as production losses and ultimately, revenue losses. Revenue losses can exceed several thousand dollars per minute. Even with rapid diagnosis, robot repairs can cause several hours of downtime. In addition, if workers have to rush to perform response tasks due to robot failures instead of their normally planned tasks, worker safety and productivity may be compromised. Studies suggest that planned tasks are 75% more efficient than response tasks. Therefore, it is desirable to avoid such production losses, revenue losses, and increased maintenance costs. [Overview of the project]

[0005] In the first embodiment, the method is Receiving a first waveform based on robot sensing using wireless sensors, To determine whether the first value of the first peak of the first waveform exceeds the first threshold. To determine whether the first waveform represents the first vibration signature, and, Perform predictive maintenance analysis of the robot when the first value exceeds the first threshold or when the first waveform represents the first vibration signature. Includes, Here, the first waveform shows the first acceleration during the first period. The first embodiment may include any combination of the following: The wireless sensor is an accelerometer. The method further includes placing the wireless sensor on a robot joint. The method further includes placing the wireless sensor near the robot's reducer. The method further includes acquiring a first waveform using the wireless sensor. The method further includes acquiring a first waveform while the robot is operating. The method further includes acquiring a first waveform while the robot is in a production cycle. The method further includes wirelessly transmitting the first waveform from the wireless sensor to a gateway. The method further includes transmitting the first waveform from the gateway to a network. The method further includes receiving the first waveform from the network. The first period is based on the robot's production cycle. The first period is approximately 55 seconds. The first value does not exceed the first threshold, and the method is, Skip determining whether the first waveform represents the first vibration signature. After skipping the step of determining whether the first waveform represents the first vibration signature, a second waveform is received based on the robot's sensing using a wireless sensor. To determine whether the second value of the second peak of the second waveform exceeds the first threshold. To determine whether the second waveform represents the second vibration signature when the second value exceeds the first threshold, and, This further includes performing predictive maintenance analysis of a robot when the second waveform represents the second vibration signature. Here, the second waveform shows the second acceleration during the second period. The method is to determine whether the first waveform represents the first vibration signature. Peak values ​​exceeding the second threshold, The PTP value of the first waveform exceeds the third threshold. The average of the values ​​in the troughs between the peaks of the first waveform that exceed the fourth threshold, or This further includes making a determination based on a first waveform containing pulses. The method involves predictive maintenance analysis. To obtain grease from components related to the first waveform of the robot, and, This further includes measuring the metal PPM in grease. The method is, The acquisition and measurement process is repeated until the amount of metallic PPM exceeds the second threshold, and This further includes repairing or replacing components when the metallic PPM exceeds a second threshold. The method further includes repeating the acquisition and measurement according to a schedule. If the first waveform does not represent the first vibration signature, the method is as follows: Skip performing predictive maintenance analysis on the robot. After skipping the predictive maintenance analysis of the robot, a second waveform based on the robot's sensing is received using wireless sensors. To determine whether the second value of the second peak of the second waveform exceeds the first threshold. To determine whether the second waveform represents the second vibration signature when the second value exceeds the first threshold, and, This further includes performing predictive maintenance analysis of a robot when the second waveform represents the second vibration signature. Here, the second waveform shows the second acceleration during the second period.

[0006] In the second embodiment, the apparatus is Memory configured to store instructions, and A processor coupled with memory, which executes instructions to the device, Using wireless sensors, the robot receives a first waveform based on its sensing. Determine whether the first value of the first peak of the first waveform exceeds the first threshold. Determine whether the first waveform represents the first vibration signature, and, Includes a processor configured to display a prompt to perform predictive maintenance analysis of a robot when a first value exceeds a first threshold or when a first waveform represents a first vibration signature, Here, the first waveform shows the first acceleration during the first period.

[0007] In the third embodiment, the computer program product is an instruction stored on a computer-readable medium which, when executed by a processor, is transmitted to the device. Using wireless sensors, the robot receives a first waveform based on its sensing. Determine whether the first value of the first peak of the first waveform exceeds the first threshold. Determine whether the first waveform represents the first vibration signature, and, Includes a command that prompts for performing predictive maintenance analysis of the robot if the first value exceeds a first threshold or if the first waveform represents a first vibration signature.

[0008] Any of the above embodiments can be combined with other embodiments to form new embodiments. These and other features will be more clearly understood from the following detailed description in conjunction with the accompanying drawings and claims. [Brief explanation of the drawing]

[0009] For a more complete understanding of this disclosure, refer to the following brief description in conjunction with the attached drawings and detailed description. Here, similar reference numbers indicate similar parts. [Figure 1] Figure 1 is a schematic diagram of the RSA system. [Figure 2] Figure 2 is a schematic diagram of the robot. [Figure 3A] Figure 3A is a diagram of a gearbox. [Figure 3B] Figure 3B shows the bearing and shaft system from the gearbox in Figure 3A. [Figure 3C] Figure 3C shows a diagram of the shaft in the bearing-shaft system shown in Figure 3B. [Figure 4A] Figure 4A is a flowchart of the RSA method. [Figure 4B] Figure 4B is a flowchart of the RSA method. [Figure 4C] Figure 4C is a flowchart of the RSA method. [Figure 5] Figure 5 is a graph of a waveform. [Figure 6] Figure 6 is a graph of another waveform. [Figure 7] Figure 7 is a graph of yet another waveform. [Figure 8] Figure 8 is a graph of yet another waveform. [Figure 9] Figure 9 is a flowchart of a simplified RSA method. [Figure 10] Figure 10 is a schematic diagram of a device.

DETAILED DESCRIPTION OF THE INVENTION

[0010] First, it should be understood that although exemplary implementations of one or more embodiments are provided below, the disclosed system and / or method may be implemented using any number of techniques, regardless of whether they are currently known or exist. The present disclosure is not limited to the exemplary implementations, drawings, and techniques shown below (including the exemplary designs and implementations illustrated and described herein), but may be modified within the scope of the appended claims and all their equivalents.

[0011] The following abbreviations apply: ASIC: Application-Specific Integrated Circuit CPU: Central Processing Unit DSP: Digital Signal Processor EO: Electrical-to-optical FANUC: Fuji Automatic Numerical Control FFT: Fast Fourier Transform FPGA: Field-Programmable Gate Array g: Standard acceleration due to gravity IR: Infrared OE: Optical-to-electrical conversion PPM: parts per million PTP: Peak-to-peak RAM: Random Access Memory RF: Radio frequency ROM: Read-only memory RSA: Robotic Signature Analysis RX: Receiver unit s: seconds SRAM: Static RAM TCAM: Ternary Content-Addressable Memory TX: Transmitter unit VFD: Variable-frequency drive °: degrees %:percent

[0012] Robots on manufacturing lines have many components such as motors, pumps, fans, and compressors, which can become incomplete due to normal wear and tear, design flaws, and manufacturing defects. Some manufacturers implement planned preventive maintenance by replacing components at specific intervals. However, such preventive maintenance risks replacing parts that are not failing, thereby incurring unnecessary costs. In addition, preventive maintenance cannot predict when a failure will occur before the component needs to be replaced.

[0013] Other manufacturers are performing predictive maintenance analysis of electrical components using IR cameras and mechanical components using ultrasonic detectors. However, such analysis does not predict failures sufficiently early or accurately. Delays in failure prediction can lead to a decrease in the functionality and accuracy of robots. In addition, IR cameras and ultrasonic detectors are very expensive. Furthermore, IR and ultrasonic data must be acquired over long periods and compared to determine trends, thus consuming a significant amount of employee time.

[0014] Furthermore, other manufacturers conduct maintenance analyses using grease samples. However, such analyses are usually reactive and do not predict failures sufficiently early. For example, manufacturers often initiate such analyses when components start making noise or robots become less precise. This necessitates robot reteaching or predicts failures only a few hours earlier, the latter carrying the risk of major failures. Delays in replacing components lead to ad-hoc planning, accelerated ordering of new components, the risk of components being backordered, inability to analyze failures, and correction of poorly performed jobs, all of which significantly extend downtime. In addition, obtaining grease samples consumes a considerable amount of employee time.

[0015] In short, current approaches to predicting defects are too costly, inaccurate, reactive, and time-intensive. Therefore, economically viable, accurate, proactive, and timely defect prediction is desirable. Such improvements could result in more time for planning, more time for ordering components, fewer components needed on-site, cost-free replacements, more time for defect analysis, and minimal corrective actions, thus leading to a reduction or elimination of defects on the production line, resulting in increased productivity and improved safety.

[0016] More specifically, since bearings are critical components of rotating systems, diagnosing bearing failures and predicting them based on signals has been a long-researched topic. Bearing signals are typically nonlinear and unstable, making analysis difficult in the time domain or frequency domain alone. On the other hand, as discussed in "Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis" by Jie Wu et al., Sensors, Volume 18, Issue 10, October 2, 2018 (incorporated herein by reference), conventionally extracted fault feature vectors with fixed dimensions can lead to insufficient or redundant diagnostic information, resulting in poor diagnostic performance. "Vibration Analysis Based Condition Monitoring for Industrial Robots" by Huanqing Han et al., Mechanisms and Machine Science, Volume 105, May 16, 2021 (incorporated herein by reference) illustrates similar difficulties associated with current approaches. Therefore, better prediction and diagnosis of bearing failures are needed.

[0017] This specification discloses embodiments for RSA. RSA receives acceleration time waveforms from wireless sensors in a robot, which can be done even while the robot is operating. RSA performs vibration analysis of the waveforms to predict failures in robot components. Vibration analysis compares the waveforms to parameters based on a library of vibration signatures. The waveforms do not require significant time or trend analysis, and failures can be detected more than six months before they occur. Since frequently failing robot components do not rotate 360° continuously or at known speeds, waveform analysis is not an intuitive solution. In addition, robots have many motors and gearboxes that operate simultaneously and perform different tasks. However, RSA demonstrates that vibration analysis is most effective for predicting failures in rotating components such as reduction gears (also referred to as gearboxes). While RSA does not predict failures with 100% accuracy, it does predict failures at least five months in advance. Five months provides ample time for manufacturers to plan, schedule, and safely carry out repairs. With such high accuracy and rapid detection, RSA can save manufacturers millions of dollars on a single production line by ensuring the avoidance of undesirable downtime. In addition, wireless sensors are significantly less expensive than IR cameras and other diagnostic tools. While gearboxes are discussed, this embodiment applies to other rotating robotic components, non-rotating robotic components, and non-robot components. Such other components include servo motors, VFDs, and servo amplifiers.

[0018] Figure 1 is a schematic diagram of the RSA system 100. The RSA system 100 includes an operator 110, a robot 120, a wireless sensor 130, a control panel 140, a teach pendant 150, a gateway 160, a network 170, a terminal device 180, and an analyst 190. The wireless sensor 130 and the gateway 160 are connected to communicate with each other, the gateway 160 and the network 170 are connected to communicate with each other, and the network and the terminal device 180 are connected to communicate with each other. The wireless sensor 130 may be an accelerometer, or it may be a wired sensor instead. The gateway 160 may be a digital gateway. The network 170 may be the Internet or another suitable network. The terminal device 180 may include software that performs the functions described below.

[0019] During operation, operator 110 teaches the robot 120 using a teach pendant 150 and operates the robot 120 using a control panel 140. The robot 120 executes a production cycle or work program to perform manufacturing tasks such as welding automotive parts. A production cycle is a process in which the robot completes one job before repeating that job. The job may be welding two components together or painting a panel. While the robot 120 is executing a production cycle, or at other appropriate times, a wireless sensor 130 collects data and transmits it wirelessly to a gateway 160. The gateway 160 transmits the data to a terminal device 180 via a network 170. An analyst 190 analyzes the data using the terminal device 180.

[0020] Figure 2 is a schematic diagram of robot 200. Robot 200 can implement robot 120 of Figure 1. Robot 200 has joints J1 205, J2 210, J3 215, J4 220, J5 225, and J6 230, and wireless sensors 235, 240, 245, 250, and 255. Figure 2 shows that robot 200 has six joints 205-230 and five wireless sensors 235-255, but robot 200 may have fewer or more joints and wireless sensors in the same or different locations. Alternatively, wireless sensors 235-255 may be wired sensors.

[0021] J1 205 rotates on the first axis, rotating the body of robot 200. J2 210 rotates on the second axis, moving the forearm of robot 200 back and forth. J3 215 rotates on the third axis, moving the upper arm of robot 200 up and down. J4 220 rotates on the fourth axis, rotating the upper arm. J5 225 rotates on the fifth axis, moving the wrist of robot 200 up and down. J6 230 rotates on the sixth axis, rotating the wrist in a circular motion.

[0022] Wireless sensor 235 is located on J2 210 or near the reduction gear inside the robot 200 and is associated with J2 210. The reduction gear is shown in Figures 3A-3C and will be described later. Wireless sensor 240 is located on J3 215 or near the reduction gear inside the robot 200 and is associated with J3 215. Wireless sensor 245 is located on J4 220 or near the reduction gear inside the robot 200 and is associated with J4 220. Wireless sensor 250 is located on J5 225 or near the reduction gear inside the robot 200 and is associated with J5 225. Wireless sensor 255 is located on J6 230 or near the reduction gear inside the robot 200 and is associated with J6 230. In this context, “nearby” means within approximately 12 inches, 6 inches, or 1 inch.

[0023] As shown in the diagram, the wireless sensors 235-255 are not positioned near all or all of the gearboxes on the six joints 205-230, but only on four joints 210, 215, 225, and 230, or near four gearboxes. Nevertheless, the wireless sensors 235-255 can still provide sufficient information to indicate a malfunction in any of the gearboxes within the robot 200. The same objective can be achieved by increasing or decreasing the number of wireless sensors.

[0024] Figure 3A is a diagram of the gearbox 300. Specifically, the gearbox 300 is a FANUC planetary gearbox used in the 2000 series gearboxes. The gearbox 300 can implement the gearbox discussed with respect to Figure 2. The gearbox 300 comprises three bearing and shaft systems 310. Figure 3B is a diagram of the bearing and shaft system 310 from the gearbox 300 in Figure 3A. The bearing and shaft system 310 includes a bearing 320 and a shaft 330. The bearing 320 is a tapered roller bearing. Figure 3B shows that the bearing 320 has failed because its surface has lost its smoothness. The gearbox may fail for reasons such as sudden stopping, contact with other objects during operation, seal failure due to insufficient maintenance of grease volume or improper application of grease, or improper programming of the robot 200 that causes the gearbox 300 to operate beyond the operating threshold. Figure 3C is a diagram of the shaft 330 in the bearing and shaft system 310 shown in Figure 3B.

[0025] Figures 4A-4C are flowcharts of RSA method 400. In Figure 4A, step 405 places a wireless sensor on a robot joint or near a robot component. The wireless sensor may be one of wireless sensors 235-255, the joint may be one of joints 205-230, and the component may be one of the reducer 300. Although one wireless sensor is discussed, method 400 can be performed simultaneously on multiple wireless sensors, for example, each of wireless sensors 235-255.

[0026] In step 410, n waveforms are acquired using wireless sensors, where n is a positive integer. The n waveforms can be acquired continuously or according to a schedule. The schedule may be once or twice a day, and every day or on weekdays. The n waveforms are acquired while the robot 200 is operating, for example, while the robot 200 is in a production cycle. The n waveforms may be similar to those shown in Figure 5. The wireless sensors may be any combination of wireless sensors 235 to 255.

[0027] Figure 5 is a graph of waveform 500. The x-axis represents time in seconds, and the y-axis represents acceleration in g. Specifically, the x-axis spans a period of 6 seconds. This period may be set based on the production cycle of robot 200 to observe different signs of component failure, which may occur between different operations of robot 200 in the production cycle. The production cycle may be approximately 55 seconds. The x-axis is in seconds, but may be in other appropriate units of time. The y-axis is in g, but may be in other appropriate units of acceleration.

[0028] As shown in the figure, waveform 500 represents substantially constant acceleration over the entire 6 seconds. For example, waveform 500 does not represent peak values ​​exceeding a known threshold, PTP values ​​exceeding a known threshold, or the average of values ​​in a valley between peaks that also exceed a known threshold. Therefore, waveform 500 suggests that the gearbox 300 is functioning correctly.

[0029] Other analyses may use frequency-based waveforms, for example, by applying FFT to waveform 500, or by applying FFT to speed measurements. However, because a gearbox has many components, and these components operate at different speeds, frequency-based waveforms cannot illustrate a single problem caused by a single component. Furthermore, speed measurements do not provide sufficiently useful data.

[0030] Returning to Figure 4A, in step 415, n waveforms are transmitted from the wireless sensor to the gateway. The gateway may be gateway 160.

[0031] In step 420, n waveforms are sent from the gateway to the network. The network could be network 170.

[0032] In Figure 4B, in step 425, n waveforms are received from the network. Terminal device 180 may also receive n waveforms.

[0033] Decision 430 determines whether the peak value of waveform i exceeds the first threshold. This determination may be performed by software in terminal device 180 or by analyst 190. The peak may be similar to that shown in Figure 6. The first threshold may be set in advance so that it is known before method 400 is started or before decision 430 is performed. Analyst 190 may save the first threshold in terminal device 180 before such a time. If the answer to decision 430 is No, method 400 skips steps 445 and 450 and proceeds to decision 435. If the answer to decision 430 is Yes, method 400 proceeds to step 445.

[0034] Figure 6 is a graph of another waveform 600. Waveform 600 is similar to waveform 500, but occurs after a first period following waveform 500. As shown, waveform 600 includes three peaks, peak 1, peak 2, and peak 3. Each of the peaks may have a value exceeding the first threshold, thus resulting in a Yes answer to decision 430. For example, the three peaks may correspond to three broken teeth in the gear assembly of the reducer 300. Thus, unlike waveform 500, which does not include peaks with values ​​exceeding the first threshold, waveform 600 suggests that the reducer 300 is not functioning properly. Waveform 600 further includes three PTPs (PTP1, PTP2, and PTP3), valley 1 between peak 1 and peak 2, valley 2 between peak 2 and peak 3, mean 1 in valley 1, and mean 2 in valley 2. These features will be described later.

[0035] Returning to FIG. 4, in determination 435, it is determined whether i < n. Software or analyst 190 can make this determination. i is an integer counter variable and i ≤ 1. If the answer to determination 435 is Yes, method 400 proceeds to step 440. If the answer to determination 435 is No, method 400 ends.

[0036] In step 440, i is incremented by 1. Software or analyst 190 can increment i. After step 440, method 400 returns to determination 430.

[0037] In step 445, waveform i is saved. Analyst 190 can save waveform i locally to terminal device 180, or save waveform i to the server using terminal device 180.

[0038] In determination 450, it is determined whether waveform i represents a vibration signature. Software or analyst 190 can make the determination based on any combination of the features shown in FIGS. 7 - 8. If the answer to determination 450 is No, method 400 proceeds to determination 435, skipping step 455. If the answer to determination 450 is Yes, method 400 proceeds to both determination 435 and step 455, with the latter performing predictive maintenance analysis.

[0039] Figure 7 is a graph of yet another waveform 700. Waveform 700 is similar to waveform 600, but occurs after a second period following waveform 600. However, waveform 700 represents an oscillation signature. Firstly, waveform 700 has three peaks, peak 1, peak 2, and peak 3, which exhibit higher values ​​than those of waveform 600. Specifically, each of the peaks may have a value exceeding the second threshold. The second threshold may be higher than the first threshold in judgment 430. Secondly, waveform 700 includes three PTPs, PTP1, PTP2, and PTP3. Each of the PTPs may have a value exceeding the third threshold. Thirdly, waveform 700 includes a trough between peak 2 and peak 3, and the average value in the trough. This average may have a value exceeding the fourth threshold. The second, third, and fourth thresholds may be preset so that they are known before method 400 is initiated or before judgment 430 is performed. The analyst 190 may save the second, third, and fourth thresholds in the terminal device 180 before such a time. Thus, the waveform 600 suggests that the reducer 300 is not functioning properly, even more so than the reducer 300 reflected in the waveform 500.

[0040] Figure 8 is a graph of yet another waveform 800. Waveform 800 is similar to waveform 700, but occurs after a third period following waveform 700. However, waveform 800 has five pulses, pulse 1, pulse 2, pulse 3, pulse 4, and pulse 5, which are not separate peaks but a grouping of peaks. Not only the mere presence of pulses, but also the presence of peak values ​​in those pulses together represent an even more pronounced vibration signature. Therefore, waveform 800 suggests that the gearbox 300 is not functioning properly to an even greater extent than the gearbox 300 reflected in waveform 700. Waveform 800 may suggest that the gearbox 300 is malfunctioning or on the verge of failure.

[0041] Therefore, any combination of features shown in Figures 7-8 may indicate an oscillation signature. These features include peak values ​​exceeding a second threshold, PTP values ​​exceeding a third threshold, the average of values ​​between peaks exceeding a fourth threshold, or the presence of pulses. While the features are discussed with respect to step 450 and Figures 7-8, the features may also be analyzed with respect to step 430 and Figure 6.

[0042] In Figure 4C, step 455 involves obtaining grease from the components. The operator 110 or analyst 190 may take the grease, and this may be done according to a schedule (for example, once a month). The grease is from the bearing 320 or shaft 330 of the gearbox 300 in Figure 3.

[0043] In step 460, the metal PPM in the grease is measured. Operator 110 or analyst 190 may measure the metal PPM by inserting a tube into the component, drawing grease from the component using a syringe, and injecting the grease into a metal dust analyzer. Operator 110 or analyst 190 may measure the metal PPM according to a schedule (e.g., once a month) along with obtaining the grease. The metal may be iron or other metal in the component. The presence of metal in the grease suggests that the components are in improper contact with each other, causing metal fragments to be scraped off and fall into the grease.

[0044] Decision 465 determines whether the metal PPM exceeds the second threshold. If the answer to Decision 465 is No, Method 400 returns to step 455. If the answer to Decision 465 is Yes, Method 400 proceeds to step 470. Collectively, steps 455, 460, and Decision 465 may be referred to as predictive maintenance analysis.

[0045] In step 470, the component is repaired or replaced. Operator 110 may send the component to the manufacturer or elsewhere for repair, or may replace the component with a new one. Step 470 may be referred to as predictive maintenance. After step 470, method 400 is completed.

[0046] Figure 9 is a flowchart of the simplified RSA method 900. In step 910, a first waveform based on robot sensing is received using a wireless sensor. The first waveform may be the same as waveform 500, the robot may be robot 200, and the wireless sensor may be one of wireless sensors 235-255. In step 920, it is determined whether the first value of the first peak of the first waveform exceeds a first threshold. In step 930, it is determined whether the first waveform represents a first vibration signature. Alternatively, step 930 may be performed when the first value exceeds the first threshold. After step 930, the simplified method 900 may proceed to step 940 or step 950. Alternatively, the simplified method 900 may proceed to step 940 and then to step 950. In step 940, a prompt is displayed to perform a predictive maintenance analysis of the robot if the first value exceeds the first threshold or if the first waveform represents a first vibration signature. In step 950, predictive maintenance analysis of the robot is performed if the first value exceeds the first threshold or if the first waveform represents the first vibration signature. Alternatively, steps 940 and 950 are performed if the first value exceeds the first threshold AND the first waveform represents the first vibration signature.

[0047] Figure 10 is a schematic diagram of the device 1000. The device 1000 may implement disclosed embodiments (e.g., a wireless sensor 130, a control panel 140, a teach pendant 150, a gateway 160, or a terminal device 180). The device 1000 comprises input ports 1010 and RX 1020 for receiving data, a processor 1030 (or a logic unit, baseband unit, or CPU) for processing data, a TX 1040 and output port 1050 for transmitting data, and a memory 1060 for storing data. The device 1000 may include OE components, EO components, or RF components coupled with input ports 1010, RX 1020, TX 1040, and output port 1050 to provide input or output of optical, electrical, or RF signals.

[0048] The processor 1030 is a combination of hardware, middleware, firmware, or software. The processor 1030 includes one or more combinations of CPU chips, cores, FPGAs, ASICs, or DSPs. The processor 1030 communicates with input ports 1010, RX1020, TX1040, output port 1050, and memory 1060. The processor 1030 includes an RSA component 1070 that implements the disclosed embodiment. The inclusion of the RSA component 1070 therefore provides a significant improvement in the functionality of the device 1000 and has the effect of switching the device 1000 to different states. Alternatively, memory 1060 stores the RSA component 1070 as instructions, and the processor 1030 executes those instructions.

[0049] Memory 1060 includes any combination of disk, tape drive, or solid-state drive. Device 1000 may use memory 1060 as an overflow data storage device to store programs when device 1000 selects them for execution, and to store instructions and data that device 1000 reads during the execution of those programs. Memory 1060 may be volatile or non-volatile, and may be any combination of ROM, RAM, TCAM, or SRAM.

[0050] A computer program product may include computer-executable instructions that are stored on a computer-readable medium and cause a device to perform any embodiment (when executed by a processor). The non-temporary medium may be memory 1060, the processor may be processor 1030, and the device may be device 1000.

[0051] The term "approximately" means a range including ±10% of the following number unless otherwise specified. If one component, device, or system is described as performing a function, multiple such components, devices, or systems may implement that function.

[0052] While multiple embodiments have been provided in this disclosure, it can be understood that the disclosed systems and methods will be embodied in many other specific forms without departing from the spirit or scope of this disclosure. These embodiments are illustrative and not restrictive and are not intended to limit the scope to the details described herein. For example, various elements or components may be combined or integrated into another system, and certain functions may be omitted or not implemented.

[0053] In addition, technologies, systems, subsystems, and methods described and illustrated as separate or distinct in various embodiments may be combined or integrated with other systems, components, technologies, or methods without departing from the scope of this disclosure. Others illustrated or discussed as being combined may be directly combined, or indirectly combined or communicate through electrical, mechanical, or other interfaces, devices, or intermediate components. Other examples of changes, substitutions, and modifications are evident to those skilled in the art and may be made without departing from the spirit and scope disclosed herein.

Claims

1. It is a method, Receiving a first waveform based on robot sensing using wireless sensors, To determine whether the first value of the first peak of the first waveform exceeds a first threshold, To determine whether the first waveform represents the first vibration signature, and Perform predictive maintenance analysis of the robot when the first value exceeds the first threshold or when the first waveform represents the first vibration signature. Includes, A method wherein the first waveform represents a first acceleration during a first period.

2. The method according to claim 1, The wireless sensor is an accelerometer, in this method.

3. The method according to claim 1, A method further comprising placing the wireless sensor in the joint of the robot.

4. The method according to claim 1, A method further comprising placing the wireless sensor near the robot's reduction gear.

5. The method according to claim 1, A method further comprising acquiring the first waveform using the wireless sensor.

6. The method according to claim 5, A method further comprising acquiring the first waveform while the robot is operating.

7. The method according to claim 5, A method further comprising acquiring the first waveform while the robot is in a production cycle.

8. The method according to claim 5, A method further comprising wirelessly transmitting the first waveform from the wireless sensor to the gateway.

9. The method according to claim 8, A method further comprising transmitting the first waveform from the gateway to the network.

10. The method according to claim 9, A method further comprising receiving the first waveform from the network.

11. The method according to claim 1, The first period is based on the production cycle of the robot, in a method.

12. The method according to claim 11, The first period is approximately 55 seconds, in this method.

13. The method according to claim 1, If the first value does not exceed the first threshold, the method is as follows: To skip determining whether the first waveform represents the first vibration signature, After skipping the step of determining whether the first waveform represents the first vibration signature, the second waveform is received using the wireless sensor based on the robot's sensing. To determine whether the second value of the second peak of the second waveform exceeds the first threshold, If the second value exceeds the first threshold, it is determined whether the second waveform represents the second vibration signature, and The method further includes performing the predictive maintenance analysis of the robot when the second waveform represents the second vibration signature, The method wherein the second waveform shows the second acceleration during the second period.

14. The method according to claim 1, Whether the first waveform represents the first vibration signature is determined by Peak value exceeding the second threshold, The peak-to-peak (PTP) value of the first waveform that exceeds the third threshold, The average of the values ​​in the troughs between the peaks of the first waveform that exceed the fourth threshold, or The first waveform including a pulse, A method that further includes making a determination based on [something].

15. The method according to claim 1, The aforementioned predictive maintenance analysis, The robot obtains grease from components related to the first waveform, and To measure the metal PPM (parts per million) in the aforementioned grease, A method that further includes doing so by means of.

16. The method according to claim 15, The acquisition and measurement described above are repeated until the metal PPM exceeds a second threshold, and A method further comprising repairing or replacing the component when the metal PPM exceeds the second threshold.

17. The method according to claim 16, A method further comprising repeating the acquisition and measurement described above according to a schedule.

18. The method according to claim 1, If the first waveform does not represent the first vibration signature, the method is performed as follows: The predictive maintenance analysis of the robot is skipped. After skipping the predictive maintenance analysis of the robot, the second waveform based on the robot's sensing is received using the wireless sensor. To determine whether the second value of the second peak of the second waveform exceeds the first threshold, If the second value exceeds the first threshold, it is determined whether the second waveform represents the second vibration signature, and The method further includes performing the predictive maintenance analysis of the robot when the second waveform represents the second vibration signature, The method wherein the second waveform shows the second acceleration during the second period.

19. It is a device, Memory configured to store instructions, and A processor coupled to the memory, which executes the instructions to the device, Using a wireless sensor, the robot receives a first waveform based on its sensing. Determine whether the first value of the first peak of the first waveform exceeds a first threshold. Determine whether the first waveform represents the first vibration signature, and A prompt to perform predictive maintenance analysis of the robot is displayed when the first value exceeds the first threshold or when the first waveform represents the first vibration signature. Includes a processor configured as follows: The apparatus wherein the first waveform represents the first acceleration during the first period.

20. A computer program product, Instructions stored on a computer-readable medium, when executed by a processor, are transmitted to the device. Using a wireless sensor, the robot receives a first waveform based on its sensing. Determine whether the first value of the first peak of the first waveform exceeds a first threshold. Determine whether the first waveform represents the first vibration signature, and A prompt to perform predictive maintenance analysis of the robot is displayed when the first value exceeds the first threshold or when the first waveform represents the first vibration signature. A computer program product that includes instructions.

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