WO2023006616A1 - An apparatus and method to estimate cycle time for processing a workpiece in an industry - Google Patents

An apparatus and method to estimate cycle time for processing a workpiece in an industry Download PDF

Info

Publication number
WO2023006616A1
WO2023006616A1 PCT/EP2022/070666 EP2022070666W WO2023006616A1 WO 2023006616 A1 WO2023006616 A1 WO 2023006616A1 EP 2022070666 W EP2022070666 W EP 2022070666W WO 2023006616 A1 WO2023006616 A1 WO 2023006616A1
Authority
WO
WIPO (PCT)
Prior art keywords
cycle time
events
signal
power signal
apparent power
Prior art date
Application number
PCT/EP2022/070666
Other languages
French (fr)
Inventor
Gunaseelan RATHINAM
Dhruv BHANDULA
Siriyur Kanteshappa ANITHA
Kathiresan Vasagam PREMA
Jai Venkat Krishna HARI RAO
Das Adhikari NIMAI CHAND
Kulampurath ATHIRA SREEKUMAR
Gajendra Mohan JHA
Naduvathra Revi KRISHNA
Gaurav AGNIHOTRI
Original Assignee
Robert Bosch Gmbh
Robert Bosch Engineering And Business Solutions Private Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Robert Bosch Gmbh, Robert Bosch Engineering And Business Solutions Private Limited filed Critical Robert Bosch Gmbh
Publication of WO2023006616A1 publication Critical patent/WO2023006616A1/en

Links

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring

Definitions

  • a production management method and system using power consumption features An electric meter is connected to an equipment machine for measuring power consumption data of the equipment machine.
  • the power consumption data during a processing cycle of the equipment machine is used as a power consumption sample.
  • the power consumption sample is uploaded to a cloud server and a plurality of feature points is set.
  • the power consumption data of the equipment machine is uploaded to the cloud server in real time and compared with the power consumption sample through feature matching, to obtain a facility utilization rate, a production efficiency and a product yield of the equipment machine, so as to calculate an overall equipment effectiveness.
  • FIG. 1 illustrates a block diagram of an apparatus to determine cycle time of processing a workpiece by a machine in a manufacturing industry, according to an embodiment of the present invention
  • Fig. 2 illustrates the method for determining cycle time of processing a workpiece by the machine in the manufacturing industry, according to the present invention.
  • Fig. 1 illustrates a block diagram of an apparatus to determine cycle time of processing a workpiece by a machine in a manufacturing industry, according to an embodiment of the present invention.
  • the apparatus 120 is shown as a part of a system 100.
  • the system 100 comprises a sensing unit 104 clamped to power cables of a work machine 102 in a non-intrusive manner.
  • the sensing unit 104 measures and collects electrical power consumption data (or power signature) of the machine 102 during a working cycle.
  • the working cycle corresponds to a time shift or time zone during which the machine 102 is operated (or is to be operated) to process a workpiece as part of manufacturing a product.
  • the apparatus 120 comprises a controller (not shown), which is configured to, receive power consumption signals, as measured by the sensing unit 104, and generates an apparent power signal.
  • the apparatus 120 is characterized by, the controller configured to filter the apparent power signal and identify prominent repetitive patterns.
  • the controller further extracts active peaks from the apparent power signal having values higher than a production threshold (explained later).
  • the controller then transforms the extracted peaks using wavelet scattering and determines the cycle time based on events identified in the transformed signal using the wavelet scattering
  • the edge device 110 functions on principles of edge computing near the source of data and thus increases the response time. [0008]
  • the controller is equipped with necessary signal detection and acquisition circuits in connection with the sensing unit 104.
  • the controller comprises memory element such as Random Access Memory (RAM) and/or Read Only Memory (ROM), Analog-to-Digital Converter (ADC) and vice-versa Digital-to-Analog Convertor (DAC), clocks, timers and at least one processor (capable of implementing machine learning) connected with the each other and to other components through communication bus channels.
  • the memory element is pre stored with logics or instructions or programs or applications or modules/models and/or threshold values, which is/are accessed by the at least one processor as per the defined routines.
  • the internal components of the controller are not explained for being state of the art, and the same must not be understood in a limiting manner.
  • the controller may also comprise communication units to communicate with the cloud server 118 through wireless or wired means such as Global System for Mobile Communications (GSM), 3G, 4G, 5G, Wi-Fi, Bluetooth, Ethernet, serial networks and the like.
  • GSM Global System for Mobile Communications
  • the controller processes the signals received by the sensing unit 104 through few modules.
  • a data processing module 106 is provided to process the measured power consumption data and generate the apparent power signal.
  • the data processing module 106 further processes the apparent power signal through a filter and generate a filtered signal for the identification of prominent repetitive patterns.
  • a cycle time estimator 108 is another module which executes the extraction of active peaks, applies wavelet scattering and determines cycle time based on the identified events. The working of the cycle time estimator 108 is explained later.
  • the filter used is Median Absolute Deviation with sliding window process and not limited to the same.
  • the filter is a Median Absolute Deviation with Sliding Window Technique (MovMAD or Mov-Mad).
  • MovMAD Median Absolute Deviation with Sliding Window Technique
  • the filter reduces noise and identifies prominent repetitive patterns.
  • the filter is customized with automatic threshold by segmentation.
  • the customized filter is generic enough to automatically identify threshold for fdtering and preserves abrupt level changes in power signal which might represent production, by attenuating ‘normal’ observational noise, excludes outliers, and keeps track of monotonic trends in signal within a reduced computation time.
  • the production threshold is calculated using K-means clustering, which is pre-determined during training process.
  • the production threshold identifies the actual production activity of the machine 102.
  • the production threshold aids in identification of the machine 102 operation/activity.
  • the higher amplitude power values are most probably related to the active production.
  • the duration and the power fluctuation varies.
  • identifying a single production can be simplified to identifying any one of the operations.
  • the spikes caused by the operations are identified visually as peaks in the power signal. However, spike could be due to other reasons as well.
  • the active peaks, or the peaks that represent probable production, are identified from the filtered signal.
  • the peaks identified in the filtered signal are clustered into two groups, to separate higher and lower amplitude power samples.
  • the least value of the cluster with highest mean is selected as threshold for identifying active peaks.
  • production threshold mm clusters_higher_power_samples )
  • the apparatus 120 is at least one selected from a group comprising an edge device 110 and a cloud server 118.
  • the apparatus 120 is the edge device 110.
  • the apparatus 120 is the cloud server 118 or a remote server.
  • the apparatus 120 is combination of the edge device 110 and the cloud server 118.
  • the apparatus 120 as the cloud server 118 is explained.
  • the cloud server 118 is in communication with the sensing unit 104 through the data processing module 106.
  • the data processing module 106 transmits the fdtered signal to the cloud server 118.
  • the cloud server 118 comprises a database 112 and a cycle time estimator 108 similar to that present in the controller.
  • the cycle time estimator 108 executes the extraction of active peaks, applies wavelet scattering and determines cycle time based on the identified events. The cycle time is then accessible to the interested party such as by the owner/operator.
  • the cycle time estimator 108 selects events from the transformed signal using Principal Component Analysis (PCA).
  • PCA Principal Component Analysis
  • the cycle time estimator 108 then clusters the events with similar characteristics using K-Means clustering or other clustering techniques. Then a distance between two consecutive events within each of the clusters is calculated. Finally, the cycle time is determined/estimated based on comparison of the distances with empirically predetermined ranges and/or rules.
  • the working of the cycle time estimator 108 is explained in more detail in stages.
  • events are identified, and feature extraction is done.
  • the active peaks that have higher power values than the production threshold are extracted from the power data consumption signal.
  • the extracted active peaks or the events representing a production are transformed using wavelet scattering.
  • the scattering representations construct invariant, stable, and informative signal representations by cascading wavelet modulus decompositions followed by lowpass filter.
  • the wavelet time scattering representations are insensitive to translations in the input signal without sacrificing class discriminability.
  • the scattering transform builds a signal representation that is invariant to transformations while preserving as much signal information as possible.
  • a convolutional network whose filters are fixed to be wavelet and lowpass averaging filters coupled with modulus nonlinearities.
  • feature selection is performed. The important events are selected using Principal Component Analysis (PC A) with two components. The analysis done on over predetermined samples (for example 40 number of machine’s data or samples) showed that more than 95% features is explained by the first two components.
  • PC A Principal Component Analysis
  • clustering is performed. The pool of events selected using PCA may contain events that do not represent production cycle. Similar events amongst the selected events are grouped together using K-Means clustering. Optimal number of groups, ‘K’, is calculated with Gap Statistic automatically.
  • cycle time is determined.
  • the system 100 is shown with a second machine 114 and an n th machine 116 with respective apparatuses 120. All the apparatuses 120 either work independently or are in communication with the cloud server 118 and operates in the similar manner as that for the machine 102. Further, the data processed by the apparatus 120 is not only the filtered signal, but also the actual raw signals measured by the sensing unit 104.
  • Fig. 2 illustrates the method for determining cycle time of processing a workpiece by the machine in the manufacturing industry, according to the present invention.
  • the method comprises plurality of steps, of which a step 202 comprises measuring power consumption data of the machine 102, which processes the workpiece (a single unit), and calculating the apparent power signal.
  • the power consumption data (or power signature) of the machine 102 is collected during the working cycle using the sensing unit 104 clamped to the power cables of the machine 102.
  • the method is characterized by, a step 204 which comprises filtering the apparent power signal for identification of prominent repetitive patterns in the filtered signal.
  • a step 206 comprises extracting active peaks from the apparent power signal having values higher than the production threshold.
  • a step 208 comprises transforming the extracted peaks using wavelet scattering.
  • a step 210 comprises determining the cycle time by processing events in transformed signal obtained from wavelet scattering. In the step 210, processing events further comprises multiple steps, of which a step 212 comprises selecting events from the transformed signal using Principal Component Analysis (PCA).
  • a step 214 comprises clustering the events with similar characteristics using K-Means clustering or other clustering algorithms.
  • a step 216 comprises calculating distance between two consecutive events within each of the clusters.
  • a step 218 comprises determining the cycle time based on comparison of the distances with predetermined ranges and/or rules. The method described above must be read in combination with the Fig. 1 and corresponding description.
  • the filtering is performed by a Median Absolute Deviation with sliding window process.
  • the production threshold is calculated using K-means clustering or other clustering techniques or algorithms.
  • the method is executed by at least one apparatus 120 selected from a group comprising the edge device 110 and the cloud server 118.
  • the edge device 110 connected/interfaced between the sensing unit 104 and the cloud server 118.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • General Factory Administration (AREA)

Abstract

The apparatus 120 is shown as a part of a system 100 which comprises a sensing unit 104 clamped to power cables of a work machine 102 in a non-intrusive manner. The sensing unit 104 measures and collects electrical power consumption data of the machine 102 during a working cycle. The apparatus 120 comprises the controller, which is configured to, receive power consumption signals, as measured by the sensing unit 104, and generates an apparent power signal. The apparatus 120 is characterized by, the controller configured to filter the apparent power signal and identify prominent repetitive patterns. The controller further extracts active peaks from the apparent power signal having values higher than a production threshold. The controller then transforms the extracted peaks using wavelet scattering and determines the cycle time based on events identified in the transformed signal using the wavelet scattering.

Description

THE PATENTS ACT, 1970
(39 of 1970) & The Patents Rules 2003
COMPLETE SPECIFICATION
(SECTION 10 and Rule 13)
1. Title of the invention:
AN APPARATUS AND METHOD TO ESTIMATE CYCLE TIME FOR PROCESSING A WORKPIECE IN AN INDUSTRY
2. Applicants: a. Name: Robert Bosch Engineering and Business Solutions Private Limited
Nationality: INDIA
Address: 123, Industrial Layout, Hosur Road, Koramangala, Bangalore - 560095, Karnataka, India b. Name: Robert Bosch GmbH Nationality: GERMANY
Address: Feuerbach, Stuttgart, Germany
Complete Specification:
The following specification describes and ascertains the nature of this invention and the manner in which it is to be performed. Field of the invention:
[0001] The present invention relates to an apparatus and method to estimate cycle time for processing a workpiece by a machine in a manufacturing industry.
Background of the invention:
[0002] A digital information is not readily available centrally in Small Medium sized Enterprises (SMEs). The two main concerns of SMEs are cost of ownership and simplification. The manual documentation of production count by operators may often lead to human data entry errors. Also, due to absence of automatic alerts, sometimes when a machine breaks down, it is not known to the industry manager/operator unless they visit the factory floor/machine. Due to idle machinery or faulty machines consuming energy in an erratic manner, energy cost is an issue that is less addressed in older machines. There exists high cost of sensor-based solution in market. The machine utilization insights that come from such complex Programmable Logic Controller (PLC) backed controls equipment require high capital costs, complex programming and masses of wiring. A time lag in report availability is faced more often. Other products in the market analyze machine data on cloud only. Hence, the reports are not real time. In many, sensor installations can be difficult to standardize, difficult to install, and are subject to degradation.
[0003] According to apriorartUS20180299944, a production management method and system using power consumption features. An electric meter is connected to an equipment machine for measuring power consumption data of the equipment machine. The power consumption data during a processing cycle of the equipment machine is used as a power consumption sample. The power consumption sample is uploaded to a cloud server and a plurality of feature points is set. The power consumption data of the equipment machine is uploaded to the cloud server in real time and compared with the power consumption sample through feature matching, to obtain a facility utilization rate, a production efficiency and a product yield of the equipment machine, so as to calculate an overall equipment effectiveness. Brief description of the accompanying drawings:
[0004] An embodiment of the disclosure is described with reference to the following accompanying drawings,
[0005] Fig. 1 illustrates a block diagram of an apparatus to determine cycle time of processing a workpiece by a machine in a manufacturing industry, according to an embodiment of the present invention, and
[0006] Fig. 2 illustrates the method for determining cycle time of processing a workpiece by the machine in the manufacturing industry, according to the present invention.
Detailed description of the embodiments:
[0007] Fig. 1 illustrates a block diagram of an apparatus to determine cycle time of processing a workpiece by a machine in a manufacturing industry, according to an embodiment of the present invention. The apparatus 120 is shown as a part of a system 100. The system 100 comprises a sensing unit 104 clamped to power cables of a work machine 102 in a non-intrusive manner. The sensing unit 104 measures and collects electrical power consumption data (or power signature) of the machine 102 during a working cycle. The working cycle corresponds to a time shift or time zone during which the machine 102 is operated (or is to be operated) to process a workpiece as part of manufacturing a product. The apparatus 120 comprises a controller (not shown), which is configured to, receive power consumption signals, as measured by the sensing unit 104, and generates an apparent power signal. The apparatus 120 is characterized by, the controller configured to filter the apparent power signal and identify prominent repetitive patterns. The controller further extracts active peaks from the apparent power signal having values higher than a production threshold (explained later). The controller then transforms the extracted peaks using wavelet scattering and determines the cycle time based on events identified in the transformed signal using the wavelet scattering The edge device 110 functions on principles of edge computing near the source of data and thus increases the response time. [0008] The controller is equipped with necessary signal detection and acquisition circuits in connection with the sensing unit 104. The controller comprises memory element such as Random Access Memory (RAM) and/or Read Only Memory (ROM), Analog-to-Digital Converter (ADC) and vice-versa Digital-to-Analog Convertor (DAC), clocks, timers and at least one processor (capable of implementing machine learning) connected with the each other and to other components through communication bus channels. The memory element is pre stored with logics or instructions or programs or applications or modules/models and/or threshold values, which is/are accessed by the at least one processor as per the defined routines. The internal components of the controller are not explained for being state of the art, and the same must not be understood in a limiting manner. The controller may also comprise communication units to communicate with the cloud server 118 through wireless or wired means such as Global System for Mobile Communications (GSM), 3G, 4G, 5G, Wi-Fi, Bluetooth, Ethernet, serial networks and the like.
[0009] In accordance to the present invention, the controller processes the signals received by the sensing unit 104 through few modules. A data processing module 106 is provided to process the measured power consumption data and generate the apparent power signal. The data processing module 106 further processes the apparent power signal through a filter and generate a filtered signal for the identification of prominent repetitive patterns. A cycle time estimator 108 is another module which executes the extraction of active peaks, applies wavelet scattering and determines cycle time based on the identified events. The working of the cycle time estimator 108 is explained later.
[0010] The filter used is Median Absolute Deviation with sliding window process and not limited to the same. The filter is a Median Absolute Deviation with Sliding Window Technique (MovMAD or Mov-Mad). The filter reduces noise and identifies prominent repetitive patterns. The filter is customized with automatic threshold by segmentation. The customized filter is generic enough to automatically identify threshold for fdtering and preserves abrupt level changes in power signal which might represent production, by attenuating ‘normal’ observational noise, excludes outliers, and keeps track of monotonic trends in signal within a reduced computation time. The fdter overcomes the difficulties in known methods such as Moving Averages, Running Medians, and other linear filters, as these known methods though track trends and attenuate Gaussian noise efficiently, but are highly vulnerable to outliers and blur level shifts. The Running Medians remove outliers and preserve shifts in a piecewise constant signal but have shortcomings in trend periods. The filter used on the present invention is more efficient for Gaussian noise, removes spikes and preserve steps even better than running medians.
[0011] The production threshold is calculated using K-means clustering, which is pre-determined during training process. The production threshold identifies the actual production activity of the machine 102. According to the present invention, the production threshold aids in identification of the machine 102 operation/activity. The higher amplitude power values are most probably related to the active production. Depending on the type and number of operations involved in the production, the duration and the power fluctuation varies. Thus, identifying a single production can be simplified to identifying any one of the operations. The spikes caused by the operations are identified visually as peaks in the power signal. However, spike could be due to other reasons as well. The active peaks, or the peaks that represent probable production, are identified from the filtered signal. The peaks identified in the filtered signal are clustered into two groups, to separate higher and lower amplitude power samples. The least value of the cluster with highest mean is selected as threshold for identifying active peaks. production threshold = mm clusters_higher_power_samples )
[0012] In accordance to an embodiment of the present invention, the apparatus 120 is at least one selected from a group comprising an edge device 110 and a cloud server 118. In an embodiment, the apparatus 120 is the edge device 110. In another embodiment, the apparatus 120 is the cloud server 118 or a remote server. In yet another embodiment, the apparatus 120 is combination of the edge device 110 and the cloud server 118.
[0013] In an embodiment of the present invention, the apparatus 120 as the cloud server 118 is explained. The cloud server 118 is in communication with the sensing unit 104 through the data processing module 106. The data processing module 106 transmits the fdtered signal to the cloud server 118. The cloud server 118 comprises a database 112 and a cycle time estimator 108 similar to that present in the controller. The cycle time estimator 108 executes the extraction of active peaks, applies wavelet scattering and determines cycle time based on the identified events. The cycle time is then accessible to the interested party such as by the owner/operator.
[0014] In accordance to an embodiment of the present invention, the operation or working of the cycle time estimator 108 is explained. The cycle time estimator 108 selects events from the transformed signal using Principal Component Analysis (PCA). The cycle time estimator 108 then clusters the events with similar characteristics using K-Means clustering or other clustering techniques. Then a distance between two consecutive events within each of the clusters is calculated. Finally, the cycle time is determined/estimated based on comparison of the distances with empirically predetermined ranges and/or rules.
[0015] The working of the cycle time estimator 108 is explained in more detail in stages. In a first stage, events are identified, and feature extraction is done. The active peaks that have higher power values than the production threshold are extracted from the power data consumption signal. The extracted active peaks or the events representing a production are transformed using wavelet scattering. The scattering representations construct invariant, stable, and informative signal representations by cascading wavelet modulus decompositions followed by lowpass filter. The wavelet time scattering representations are insensitive to translations in the input signal without sacrificing class discriminability. The scattering transform builds a signal representation that is invariant to transformations while preserving as much signal information as possible. It is defined as a convolutional network whose filters are fixed to be wavelet and lowpass averaging filters coupled with modulus nonlinearities. In a second stage, feature selection is performed. The important events are selected using Principal Component Analysis (PC A) with two components. The analysis done on over predetermined samples (for example 40 number of machine’s data or samples) showed that more than 95% features is explained by the first two components. In a third stage, clustering is performed. The pool of events selected using PCA may contain events that do not represent production cycle. Similar events amongst the selected events are grouped together using K-Means clustering. Optimal number of groups, ‘K’, is calculated with Gap Statistic automatically. In a fourth stage, cycle time is determined. The cycle time is the time required to produce a single unit. Amongst the clusters generated from the previous stage, the distance between two consecutive events within a cluster represent probable cycle times. The highly likely cycle time of the product is chosen by binning the probable cycle times into ranges and rules generated by analyzing over few samples (for example 80 number of machine’s data or samples) of product data.
[0016] In accordance to an embodiment of the present invention, the system 100 is shown with a second machine 114 and an nth machine 116 with respective apparatuses 120. All the apparatuses 120 either work independently or are in communication with the cloud server 118 and operates in the similar manner as that for the machine 102. Further, the data processed by the apparatus 120 is not only the filtered signal, but also the actual raw signals measured by the sensing unit 104.
[0017] Fig. 2 illustrates the method for determining cycle time of processing a workpiece by the machine in the manufacturing industry, according to the present invention. The method comprises plurality of steps, of which a step 202 comprises measuring power consumption data of the machine 102, which processes the workpiece (a single unit), and calculating the apparent power signal. The power consumption data (or power signature) of the machine 102 is collected during the working cycle using the sensing unit 104 clamped to the power cables of the machine 102. The method is characterized by, a step 204 which comprises filtering the apparent power signal for identification of prominent repetitive patterns in the filtered signal. A step 206 comprises extracting active peaks from the apparent power signal having values higher than the production threshold. A step 208 comprises transforming the extracted peaks using wavelet scattering. A step 210 comprises determining the cycle time by processing events in transformed signal obtained from wavelet scattering. In the step 210, processing events further comprises multiple steps, of which a step 212 comprises selecting events from the transformed signal using Principal Component Analysis (PCA). A step 214 comprises clustering the events with similar characteristics using K-Means clustering or other clustering algorithms. A step 216 comprises calculating distance between two consecutive events within each of the clusters. A step 218 comprises determining the cycle time based on comparison of the distances with predetermined ranges and/or rules. The method described above must be read in combination with the Fig. 1 and corresponding description.
[0018] The filtering is performed by a Median Absolute Deviation with sliding window process. The production threshold is calculated using K-means clustering or other clustering techniques or algorithms. The method is executed by at least one apparatus 120 selected from a group comprising the edge device 110 and the cloud server 118. The edge device 110 connected/interfaced between the sensing unit 104 and the cloud server 118.
[0019] According to the present invention, an ensemble based approach for non- intrusive machine and production monitoring in manufacturing industries is provided. Specifically, the present invention provides analytics solutions for improving OEE in manufacturing industries through an Intemet-of-Things (IoT) and Artificial Intelligence (AI) based asset and energy monitoring system. The data processing module 106 further configured to calculate and generate an apparent power signal from the measured consumption data (active power and the reactive power). The approach used for this invention is termed as Ensemble -based Machine Learning and Signal Detection Techniques (EMLSD) for Non-intrusive Machine and Production Monitoring in Manufacturing Industries. In the present invention, the result in terms of OEE is generated using a tree-based approach.
[0020] It should be understood that embodiments explained in the description above are only illustrative and do not limit the scope of this invention. Many such embodiments and other modifications and changes in the embodiment explained in the description are envisaged. The scope of the invention is only limited by the scope of the claims.

Claims

We claim:
1. An apparatus (120) to estimate cycle time for processing a workpiece by a machine (102) in a manufacturing industry, said apparatus (120) comprises a controller configured to receive power consumption signals, as measured by a sensing unit (104), and generate an apparent power signal, characterized in that, said controller configured to, filter said apparent power signal for identification of prominent repetitive patterns; extract active peaks from said apparent power signal having values higher than a production threshold; transform said extracted peaks using wavelet scattering, and determine said cycle time based on events identified in said transformed signal using said wavelet scattering.
2. The apparatus (120) as claimed in claim 1, wherein to determine said cycle time, said controller configured to, select events from said transformed signal using Principal Component Analysis (PCA); cluster said events with similar characteristics using k-means clustering; calculate distance between two consecutive events within each of said clusters, and determine said cycle time based on comparison of said distances with predetermined ranges and/or rules.
3. The apparatus (120) as claimed in claim 1, wherein said filter uses a Median Absolute Deviation with sliding window process.
4. The apparatus (120) as claimed in claim 1, wherein said production threshold is calculated using K-means clustering.
5. The apparatus (120) as claimed in claim 1 is at least one selected from a group comprising an edge device (110) and a cloud server (118).
6. A method for determining cycle time of processing a workpiece in a manufacturing industry, said method comprising the steps of: measuring power consumption data of a machine which processes said workpiece, and calculating an apparent power signal, characterized by, fdtering said apparent power signal and identifying prominent repetitive patterns; extracting active peaks from said apparent power signal having values higher than a production threshold; transforming said extracted peaks using wavelet scattering, and determining said cycle time by processing events in a transformed signal obtained from wavelet scattering.
7. The method as claimed in claim 6, wherein processing said events comprises selecting events from said transformed signal using Principal Component Analysis (PCA); clustering said events with similar characteristics using k-means clustering; calculating distance between two consecutive events within each of said clusters, and determining said cycle time based on comparison of said distances with predetermined ranges and/or rules.
8. The method as claimed in claim 6, wherein said filtering is performed by a Median Absolute Deviation with sliding window process.
9. The method as claimed in claim 6, wherein said production threshold is calculated using K-means clustering.
10. The method as claimed in claim 6 is executed by at least one apparatus (120) selected from a group comprising an edge device (110) and a cloud server (118).
PCT/EP2022/070666 2021-07-29 2022-07-22 An apparatus and method to estimate cycle time for processing a workpiece in an industry WO2023006616A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
IN202141034096 2021-07-29
IN202141034096 2021-07-29

Publications (1)

Publication Number Publication Date
WO2023006616A1 true WO2023006616A1 (en) 2023-02-02

Family

ID=83004976

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2022/070666 WO2023006616A1 (en) 2021-07-29 2022-07-22 An apparatus and method to estimate cycle time for processing a workpiece in an industry

Country Status (1)

Country Link
WO (1) WO2023006616A1 (en)

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180299944A1 (en) 2017-04-14 2018-10-18 National Tsing Hua University Production Management Method and System Using Power Consumption Features

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180299944A1 (en) 2017-04-14 2018-10-18 National Tsing Hua University Production Management Method and System Using Power Consumption Features

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
XIANG MIN CHEE ET AL: "Intelligent identification of manufacturing operations using in-situ energy measurement in industrial injection moulding machines", IECON 2011 - 37TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY, IEEE, 7 November 2011 (2011-11-07), pages 4284 - 4289, XP032105139, ISBN: 978-1-61284-969-0, DOI: 10.1109/IECON.2011.6120012 *
YANG ZHENGNI ET AL: "Transfer Learning Based Rolling Bearing Fault Diagnosis", 2021 IEEE 10TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS), IEEE, 14 May 2021 (2021-05-14), pages 354 - 359, XP033930930, DOI: 10.1109/DDCLS52934.2021.9455448 *

Similar Documents

Publication Publication Date Title
CN113253037B (en) Current ripple-based edge cloud cooperative equipment state monitoring method and system and medium
CN110133500B (en) Motor online monitoring and fault precursor diagnosis system and method based on multi-layer architecture
CN106649755B (en) Threshold value self-adaptive setting abnormity detection method for multi-dimensional real-time power transformation equipment data
US10311703B1 (en) Detection of spikes and faults in vibration trend data
CN116483015B (en) Workshop equipment monitoring method, device, equipment and storage medium
CN110469496B (en) Intelligent early warning method and system for water pump
CN115639470A (en) Generator monitoring method and system based on data trend analysis
CN117473514B (en) Intelligent operation and maintenance method and system of industrial control system
CN116244765A (en) Equipment maintenance management method based on industrial Internet
CN114611744A (en) Machine tool management method, machine tool management system, and medium
CN108345450A (en) The method for generating the software architecture for managing data
WO2019197082A1 (en) Cause determination of anomalous events
CN113887749A (en) Cloud edge cooperation-based multi-dimensional monitoring and disposal method, device and platform for power internet of things
CN114993640A (en) Equipment state monitoring method, device, equipment and computer storage medium
CN114611745A (en) Machine tool evaluation method, machine tool evaluation system, and medium
CN118134458A (en) Intelligent equipment monitoring and maintenance system
CN112446389A (en) Fault judgment method and device
CN111176226A (en) Automatic analysis method for alarm threshold of equipment characteristic parameter based on operation condition
WO2023006616A1 (en) An apparatus and method to estimate cycle time for processing a workpiece in an industry
CN110889395B (en) Machine learning-based mechanical motion recognition method and system
CN116448234A (en) Power transformer running state voiceprint monitoring method and system
Baier et al. Identifying failure root causes by visualizing parameter interdependencies with spectrograms
CN115421447B (en) Method, system and device for evaluating and controlling time-energy efficiency of numerical control machine tool
KR101984257B1 (en) Cloud service based big data analysing system and method therein
KR20200129870A (en) Data collection and analysis monitoring system and control method thereof

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22757513

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22757513

Country of ref document: EP

Kind code of ref document: A1