MACHINE LEARNING-ENABLED PREDICTIVE HEALTH ANALYSIS FOR INDUSTRIAL AUTOMOBILE TURRET MILLING MACHINES USING SENSOR TECHNOLOGY WITH LORA AND XBEE MULTIHOP IOT NETWORK

Abstract

A machine learning-enabled predictive health analysis for industrial automobile turret milling machines using sensor technology with lora and xbee multihop iot network comprises Datacollector Mote device, is there a mention that it is equipped with an Atmega128 board, LoRa RF Module, temperature sensor, vibration sensor, current sensor, buzzer and power supply, the datacollector mote device allows for data acquisition on time, transmission of data and therefore can be used to monitor key machine parameters effectively enhancing the predictive maintenance of turret milling machines the Routing Mote, made up of Atmega128 board, LoRa RF Module, XBee Module, toucher display and power source, has additional features which are the relaying of information through the multihop IoT network on the site of the deployment, visualization of the data and the operators receiving alerts to ensure that in the event any machine has an abnormal working condition, the operators comes into contact with the machines very fast.

Specification

Description:FIELD OF THE INVENTION
This invention relates to machine learning-enabled predictive health analysis for industrial automobile turret milling machines using sensor technology with lora and xbee multihop iot network.
BACKGROUND OF THE INVENTION
The new development focuses on a new intelligent predictive maintenance module specifically created for industrial automobile turret milling machines which incorporates the use of a network of special Motes for simultaneous surveillance of basic machine parameters such as temperature, vibration and current. Information is collected and transmitted using a multi hop IoT platform which determines and uses the best possible communication system that can be used to transmit data over long distances and under different environments. Once the data is transmitted, it is processed in a specific cloud platform that uses machine learning to evaluate the current data with previously stored data in order to predict possible breakdowns and advise on the machine's condition. Such data is capable of being posted on the secure dashboard and availed to the relevant users either in real time or historical format, and an onboard monitor will notify the operator of the irregular condition upon its occurrence. This system enables scheduled maintenance of machinery and increases the equipment's operational availability, safety and efficiency by allowing timely intervention in case the first signs of wear and tear or defects appear.
This project looks at the problem of the system of preventive, rationed maintenance at given intervals, which is relevant for the reliability of industrial automobile turret milling machines as neglecting some constant heat, vibration or power variation leads to breakdowns, poor quality and even hazards in factory. Scheduled manual inspection and maintenance, as practice shows, is not always efficient and fails to detect early failure mechanisms in machines with heavy workloads. This innovation is characterized by a MDM approach, which combines the monitoring of the operating status of the machine and the process of performing its functional duties. It collects vital performance indicators, so the operators can detect the cause of the anomaly and receive an actionable response when it's not too late. In addition, as preventative maintenance is possible, machine downtimes are kept low, maintenance expenses are minimized, asset life and reliability is improved.
US10239131B2: The present invention relates to embodiments of a machine tool, in particular a multi-spindle milling machine, comprise a machine frame, a workpiece clamping device for clamping a workpiece, an axis slide assembly arranged on the machine frame, and a spindle carrier assembly which is arranged on the machine frame and has at least two tool-carrying work spindles. The axis slide assembly is configured to linearly move the workpiece clamped at the workpiece clamping device by way of three controllable linear axes X, Y and Z. The work spindles are arranged at a turret which can be rotated or swiveled about a turret axis at respectively equal distance from the turret axis, and the spindle axes of the work spindles are aligned or can be aligned in parallel to one another and in parallel to the turret axis.
RESEARCH GAP: The novelty of this system is its multihop IoT network integrating LoRa and XBee for predictive health analysis in industrial automobile turret milling machines.
KR20170035342A: The present invention relates to a machine tool, in particular, a multi-spindle milling machine. According to the present invention, the machine tool comprises: a machine frame (1); a working stuff clamping device (2) to clamp a working stuff (WS); a shaft sliding assembly (3) disposed on the machine frame (1); and a spindle carrier assembly disposed on the machine frame (1) and including at least two tool-transfer working spindles (S1, S2). The shaft sliding assembly (3) linearly moves the working stuff (WS) clamped by the working stuff clamping device (2) by three controllable linear axes (X, Y, Z). The work spindles (S1, S2) are disposed on a turret (4) capable of rotating or swiveling around a turret shaft in an identical distance from the turret shaft. Thus, the spindle axes of the working spindle (S1, S2) are arranged in parallel to each other, and are aligned or can be aligned in parallel to the turret shaft.
RESEARCH GAP: The novelty of this system is its multihop IoT network integrating LoRa and XBee for predictive health analysis in industrial automobile turret milling machines.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The explanation of this proposal revolves the network of Motes, which makes use of the significant parameters of the industrial automobile turret milling machines through monitoring and evaluation. It starts from the Mote called the Datacollector, which automatically finds the values of temperature, vibration, and electrical current. Such Mote receives and transmits information via wireless and adaptive networking technologies allowing for centroschafting of machines. This Mote is also equipped with telemetry devices scanning for abnormal deviation and therefore notify the operators as to what state the machine is at that point in time. In this case, the communication between devices takes place through synergetic connections based on multihop IoT architecture which chooses automatically any absent links. This task is undertaken by the Routing Mote, which comprises near field and far field communication elements and focuses at linking the most appropriate available medium for communication over a distance. Also, the routing Mote has a touch interface so that the operators on site can respond immediately to any alerts and easily interact with the routing Mote. In this manner, a robust and versatile telecommunications loop is built which enables constant surveillance and rapid retention of the information even in the hostile environment of an industrial enterprise.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The explanation of this proposal revolves the network of Motes, which makes use of the significant parameters of the industrial automobile turret milling machines through monitoring and evaluation. It starts from the Mote called the Datacollector, which automatically finds the values of temperature, vibration, and electrical current. Such Mote receives and transmits information via wireless and adaptive networking technologies allowing for centroschafting of machines. This Mote is also equipped with telemetry devices scanning for abnormal deviation and therefore notify the operators as to what state the machine is at that point in time. In this case, the communication between devices takes place through synergetic connections based on multihop IoT architecture which chooses automatically any absent links. This task is undertaken by the Routing Mote, which comprises near field and far field communication elements and focuses at linking the most appropriate available medium for communication over a distance. Also, the routing Mote has a touch interface so that the operators on site can respond immediately to any alerts and easily interact with the routing Mote. In this manner, a robust and versatile telecommunications loop is built which enables constant surveillance and rapid retention of the information even in the hostile environment of an industrial enterprise.
Once the data is transmitted over the multihop network, it is received by the DataGateway Mote, which aggregates and sends this data to the cloud. The incoming data relationships undergo patterns analysis through the automatic scanning of the parameters that denote the prospects of wear, inefficiencies or even malfunctioning of the cloud server. The information obtained is then used to make appropriate forecasts that are managed by secure web dashboards accessible to authorized users. Through the integration of such analytics with artificial intelligence, the system is able to extend beyond just contemporary information but also includes historical data on machine nuttiness so so as to estimate how much average precautionary measures would be necessary to maintain over time. When the system detects such risk, it alarms the operator in order to prevent additional damage. This web dashboard simplifies the management of repair work progress by enabling preauthorized users to conduct such activities from any place. With this improvement, both detailed information and forecasting are possible, which helps to avoid an operational decrease and risk factors, requires timely measures, and allows effective maintenance processes to be performed.
BEST METHOD OF WORKING
The Datacollector Mote device, Is there a mention that it is equipped with an Atmega128 board, LoRa RF Module, temperature sensor, vibration sensor, current sensor, buzzer and power souce? The datacollector mote device allows for data acquisition on time, transmission of data and therefore can be used to monitor key machine parameters effectively enhancing the predictive maintenance of turret milling machines.
The Routing Mote, made up of Atmega128 board, LoRa RF Module, XBee Module, toucher display and power source, has additional features which are the relaying of information through the multihop IoT network on the site of the deployment, visualization of the data and the operators receiving alerts to ensure that in the event any machine has an abnormal working condition, the operators comes into contact with the machines very fast.
The DataGateway Mote, Through Jetson Nano board, XBEE module powered and Indicator LED, and power source, is functional and has the capacity to deliver significant amount of data to a custom cloud through hot spot in wifi network which then allows machine learning model and AI based health recommendations to be presented to authorize people only.
The Routing Operator Mote is constantly providing the Routing Operator with a simple touch screen to monitor the machine state and work with it accordingly in order to facilitate the work with machine conditions management and intervene before any adverse occurrences.
Two radio modules, XBee and LoRa RF, incorporated in the Routing Mote, enable data transmission through a reliable, long-distance multi-hop network which ensures continuous monitoring of the machine's parameters in remote locations with weak networks as well as provides the capability of transferring the data across the IoT network with no interruption or loss of communication.
ADVANTAGES OF THE INVENTION
1. The break down of the machine systems is also investigated since the Atmega128 board with a temperature, a vibration and a current sensor, which is a constituent of the Datacollector Mote, enables monitoring of critical parameters in real time.
2. The Routing Mote contains Atmega128 board, LoRa RF Module and XBee modules enable a good multihop communication over long distances. industry because such conditions are more likely to make communication stable even when there are adverse operating conditions.
3. This configuration makes it possible to assess the health of a machine in a Predictive mode thereby reducing any sudden breakdowns and enhancing machine life. The DataGateway Mote forwards the data to the cloud server through the Jetson Nano whose board is WiFi enabled, meanwhile there is also an AI recommendation which utilizes machine-learning based deep analysis.
4. Since the Routing Mote contains a touch screen and a buzzer, it is possible to blast alarms on location and enable operators to promptly respond to any irregularities. This configuration aids in enhancing operational efficiency as it enables real-time monitoring and the dispatching of alerts simultaneously.
5. The DataGateway Mote users who have the proper permissions can monitor the status of the sickness of the machine from the web dashboard offered by the cloud server. This type of remote viewing enhances the flexibility in planning the maintenance activities, as well as the lead time for making a decision.
6. The integration of the components XBee Module and LoRa RF Module in Routing Mote guarantees dependable data transfer operations for a multihop IoT networks to enable surveillance in industrial areas despite poor network connectivity.
, Claims:1. A machine learning-enabled predictive health analysis for industrial automobile turret milling machines using sensor technology with lora and xbee multihop iot network comprises Datacollector Mote device, is there a mention that it is equipped with an Atmega128 board, LoRa RF Module, temperature sensor, vibration sensor, current sensor, buzzer and power supply, the datacollector mote device allows for data acquisition on time, transmission of data and therefore can be used to monitor key machine parameters effectively enhancing the predictive maintenance of turret milling machines.
2. The machine as claimed in claim 1, wherein the Routing Mote, made up of Atmega128 board, LoRa RF Module, XBee Module, toucher display and power source, has additional features which are the relaying of information through the multihop IoT network on the site of the deployment, visualization of the data and the operators receiving alerts to ensure that in the event any machine has an abnormal working condition, the operators comes into contact with the machines very fast.
3. The machine as claimed in claim 1, wherein the DataGateway Mote, Through Jetson Nano board, XBEE module powered and Indicator LED, and power source, is functional and has the capacity to deliver significant amount of data to a custom cloud through hot spot in wifi network which then allows machine learning model and AI based health recommendations to be presented to authorize people only.
4. The machine as claimed in claim 1, wherein the Routing Operator Mote is constantly providing the Routing Operator with a simple touch screen to monitor the machine state and work with it accordingly in order to facilitate the work with machine conditions management and intervene before any adverse occurrences.
5. The machine as claimed in claim 1, wherein two radio modules, XBee and LoRa RF, incorporated in the Routing Mote, enable data transmission through a reliable, long-distance multi-hop network which ensures continuous monitoring of the machine's parameters in remote locations with weak networks as well as provides the capability of transferring the data across the IoT network with no interruption or loss of communication.

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