CLOUD-INTEGRATED FFT ANALYSIS AND XBEE-ENABLED MACHINE LEARNING FOR PREDICTIVE MAINTENANCE OF DIE-CASTING MACHINES IN AUTOMATED MANUFACTURING

Abstract

A Cloud-integrated fft analysis and xbee-enabled machine learning for predictive maintenance of die-casting machines in automated manufacturing comprises Data Collection Node, which has vibration, temperature, strain, and current sensing modules as well as a wireless communication module, allows for real-time monitoring of parameters of die-casting machines and helps in acquiring data as well as recognizing abnormal behavior of the machines at the initial stages, this node also has an autonomous power source built in, which guarantees that it will carry out its tasks in an industrial setting without any disruptions the Routing Node, which comprises a control unit with two wireless communication modules and an autonomous power source, enhances the distance for data transmission between the Data Collection Node and the SolarGateway Node, this particular node is very well equipped to work as a data relay and is able to provide long range connectivity, something that is necessary in complex manufacturing systems.

Specification

Description:FIELD OF THE INVENTION
This invention relates to cloud-integrated fft analysis and xbee-enabled machine learning for predictive maintenance of die-casting machines in automated manufacturing.
BACKGROUND OF THE INVENTION
This innovative concept provides an effective preventive maintainance method applicable to automated production: machines for die-casting. Its implementation is made possible by cloud-based services, fft analysis as well as the capability to employ machine learning enabling the technology to constantly visualize the working of the machine and give predictive forecasts in order to avoid breakdowns. The configuration includes a network of nodes that are purposely created with specific environmental and operational sensors, which sensors collect and preprocesses information at the edge device to send it through the multi-tiered communication framework. The configuration further incorporates a main network control unit which has a screen and alarm to aid in data communication, visualization and management. This methodology improves the uptime of the machine, decreases maintenance costs and allows for an increased level of automation of the site.
The invention tackles a very important issue of predictive maintenance in automated manufacturing where machine breakdowns are unanticipated and can result in production loss, high-cost repairs and downtimes. Preventive maintenance approaches mostly depend on a fixed timetable or reaction which often leads to wastage of resources and failure to detect or notice early indicators of wear and tear or defects. Employing real-time monitoring and machine learning, this innovation seeks to sense more subtle changes in machine functioning that could otherwise result in failures. This maintenance strategy will help reduce the number of disruptions to our operations, cause a decrease in maintenance costs and increase the effective life of the die-casting machines which in turn ensures uninterrupted manufacturing processes and reliability to the entire system.
CA2838549C: A piston for a die-casting machine, in particular with a cold chamber, comprises a stem which extends from a proximal end to a distal end along a piston axis and a piston head which extends from the distal end of the stem and which has a side wall with at least one sealing area suitable to form a seal on the wall of said container of the press. A lubrication circuit suitable for favouring the sliding of the piston comprises first lubrication ducts made in the stem and ending at the distal end of said stem, and second ducts made in the piston head, fluidically communicating with said first ducts and coming out in the lateral wall at least in correspondence with said sealed area.
RESEARCH GAP: Cloud-integrated FFT analysis combined with XBee-enabled machine learning for predictive maintenance in automated die-casting machines is the novelty of this system.
CN204710798U: The utility model discloses a kind of automobile component die casting machine foundry goods rinsing table, comprise cleaning table top, cleaning hairbrush and shower, lifting column is connected with on the downside of cleaning table top, lifting column bottom connecting fluid cylinder pressure, cleaning table top is located at inside tank, tank left and right sides inwall is all provided with ultrasonic cleaner, support is provided with on the upside of tank, support bottom is fixedly connected on tank, support upper center is vertically provided with rotating shaft, rotating shaft bottom connects horizontally disposed brush handle, cleaning hairbrush is installed with on the downside of brush handle, the utility model automobile component die casting machine foundry goods rinsing table, adopt circulation hydro-peening, lifting is scrubbed and is cleaned the foundry goods after die casting with Ultrasonic Cleaning, cleaning performance is good, efficiency is high, without the need to manual operation, reduce labour intensity, and saved resource, reduce use cost, can carry out air-dry to foundry goods after cleaning in addition, effectively prevent foundry goods to get rusty, and rinsing table conveniently moving is quick, practicality and convenience high.
RESEARCH GAP: Cloud-integrated FFT analysis combined with XBee-enabled machine learning for predictive maintenance in automated die-casting machines is the novelty of this system.
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.
This technology can best be referred to as a cloud enabled predictive maintenance system which is meant for die-casting machines deployed in automated manufacturing setups. It utilizes FFT (Fast Fourier Transform) analysis, neural networks and Monitoring of Operations to foresee and prevent any incipient mechanical damage and eventual outward breakdown. Thin data collection node composed of sensors for vibration, temperature, strain, and current monitoring. Vibration data undergoes fft analysis with a view of tracking volitional machine behavior deviation. This node performs the transformations within the edge computing framework, thus limiting the need for cloud-related latency and bandwidth to a single overhead transmission. Routing Node This 'Routing' node has a dual role: it collects instrumented data from the data collection node and relays it to the gateway node over the wide communication channel. It enables both nodes to communicate with each other using wireless communications seamlessly despite most of the nodal points located in the middle of a manufacturing floor. Redundancies of this nature, such wireless meshed connections, retain the reliability of the information stream on the central system. The gateway node comprises cloud integration, a UI, and alert functions. This node collects and integrates data from the entire sensors, analyses the information for outliers and applies machine learning models for predictive analytics. As long as there is a critical abnormality, both visual and audio cues are deployed, in particular, a touch screen that shows the abnormal status. Also, this node sends the processed information to the cloud to be archived and analyzed later, making it possible to have a more detailed picture of the machine's functioning through remote access and its evolution over time.
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.
This technology can best be referred to as a cloud enabled predictive maintenance system which is meant for die-casting machines deployed in automated manufacturing setups. It utilizes FFT (Fast Fourier Transform) analysis, neural networks and Monitoring of Operations to foresee and prevent any incipient mechanical damage and eventual outward breakdown. Thin data collection node composed of sensors for vibration, temperature, strain, and current monitoring. Vibration data undergoes fft analysis with a view of tracking volitional machine behavior deviation. This node performs the transformations within the edge computing framework, thus limiting the need for cloud-related latency and bandwidth to a single overhead transmission. Routing Node This 'Routing' node has a dual role: it collects instrumented data from the data collection node and relays it to the gateway node over the wide communication channel. It enables both nodes to communicate with each other using wireless communications seamlessly despite most of the nodal points located in the middle of a manufacturing floor. Redundancies of this nature, such wireless meshed connections, retain the reliability of the information stream on the central system. The gateway node comprises cloud integration, a UI, and alert functions. This node collects and integrates data from the entire sensors, analyses the information for outliers and applies machine learning models for predictive analytics. As long as there is a critical abnormality, both visual and audio cues are deployed, in particular, a touch screen that shows the abnormal status. Also, this node sends the processed information to the cloud to be archived and analyzed later, making it possible to have a more detailed picture of the machine's functioning through remote access and its evolution over time.
The maintenance functions in use today are either undertaken after failure has already occurred or are done regularly but they can be scheduled even when they are not absolutely needed leading to wastage of resources. This invention provides an alternative which converts maintenance from reactive to preventive. The system decreases costly outages by monitoring the machines in real time and finding issues before they cause the machines to fail, thus reducing repair costs and increasing the useful life of the machines. The system's high reliability of communication between nodes and the cloud is based on a dual layered structure that includes short and long range RF modules. With solar energy reserves, each node is independent and can operate in 'green' mode and does not need much additional energy. Owing to these technical characteristics, the technological system has high reliability, the accuracy of data, and efficiency in predictive maintenance. The innovation provides a powerful advanced monitoring technology along with ease of use, working in real time and having warning signals useful for factory operators and maintenance teams to act quickly when necessary. The fact that it operates on solar energy and is cloud-based allows it to seamlessly fit various production settings, offering a cost-effective solution and remote management. Such systems have helped organizations manage their maintenance practices better, reduce operational costs, and increase the reliability of machines, which is important for a contemporary automated production environment.
BEST METHOD OF WORKING
The Data Collection Node, which has vibration, temperature, strain, and current sensing modules as well as a wireless communication module, allows for real-time monitoring of parameters of die-casting machines and helps in acquiring data as well as recognizing abnormal behavior of the machines at the initial stages. This node also has an autonomous power source built in, which guarantees that it will carry out its tasks in an industrial setting without any disruptions.
The Routing Node, which comprises a control unit with two wireless communication modules and an autonomous power source, enhances the distance for data transmission between the Data Collection Node and the SolarGateway Node. This particular node is very well equipped to work as a data relay and is able to provide long range connectivity, something that is necessary in complex manufacturing systems.
The SolarGateway Node is designed as a main interface for collecting and displaying real-time maintenance data and runs an interface control of any single unit, two communication modules, a screen, an alarm system, and three independent power supplies. It gives factory personnel the capability to check the status of machines and raise alerts instantly, whereby reducing the need to undertake routine maintenance and increasing the reliability of operations.
The Solar Gateway Node hosts a cloud communication module that permits data transfer to cloud-based servers for the purpose of monitoring. Using this module, the maintenance teams are able to monitor machine status from any place, which makes it possible to respond to the alerts in the system in a timely manner and also capitalize on the facts available during the decision making process.
A touch display is one of the components of the Solar Gateway Node that offers visualization of machine health and a graphical interface for instant interaction. These displays make it easy for operators to determine the system status, be notified of any alarms and potential maintenance requirements, and act immediately increasing interaction with the user and efficiency in monitoring the system.
A solar power supply has been embedded with every node to provide a durable and reliable off grid power solution that can be used in energy-deprived regions and keep the system running. Such a source of power also greatly lowers the need for external mains, thus increasing reliability of the system and compatibility for various industrial environments.
A wireless communication module, that was included in the Data Collection and Routing Nodes, allows for low power data transmission even at close distances. By using this module, real-time transmission of sensor measurements captured from machines to intermediate nodes is possible, which allows a smooth tracking of the machine's operating status and data of all types.
The Long-Range RF Communication Module, installed in the Routing Node and SolarGateway Node, has enhanced data transmission over larger industrial areas. This module enables the integration of data collected from distant nodes into the central system which aids in effective supervision over large distances while providing the required connection within the predictive maintenance system.
ADVANTAGES OF THE INVENTION
1. This system offers the possibility of predictive maintenance through constant monitoring and machine learning which facilitates advanced warning on emerging problems, thus minimizing unexpected downtime and increasing the life of the apparatus.
2. Since the system takes a proactive position towards maintenance, it helps in SO repair and replacement only as necessary and also reduces the wastage of resources available in the course of performing scheduled maintenance.
3. Better data improves the rate of machine availability and operational efficiency since machines are provided with relevant information which enables better decision making and smoother production processes.
4. The system uses solar-powered nodes which eliminate the need for an external power supply and makes it eco-friendly hence it can be used in various industrial settings.
5. The cloud-based design allows for multiple machines and locations to be deployed and scaled easily, while the remote monitoring feature allows maintenance crews to view machine information anywhere which improves management and response time.
, Claims:1. A Cloud-integrated fft analysis and xbee-enabled machine learning for predictive maintenance of die-casting machines in automated manufacturing comprises Data Collection Node, which has vibration, temperature, strain, and current sensing modules as well as a wireless communication module, allows for real-time monitoring of parameters of die-casting machines and helps in acquiring data as well as recognizing abnormal behavior of the machines at the initial stages, this node also has an autonomous power source built in, which guarantees that it will carry out its tasks in an industrial setting without any disruptions.
2. The machine as claimed in claim 1, wherein the Routing Node, which comprises a control unit with two wireless communication modules and an autonomous power source, enhances the distance for data transmission between the Data Collection Node and the SolarGateway Node, this particular node is very well equipped to work as a data relay and is able to provide long range connectivity, something that is necessary in complex manufacturing systems.
3. The machine as claimed in claim 1, wherein the SolarGateway Node is designed as a main interface for collecting and displaying real-time maintenance data and runs an interface control of any single unit, two communication modules, a screen, an alarm system, and three independent power supplies, it gives factory personnel the capability to check the status of machines and raise alerts instantly, whereby reducing the need to undertake routine maintenance and increasing the reliability of operations.
4. The machine as claimed in claim 1, wherein the Solar Gateway Node hosts a cloud communication module that permits data transfer to cloud-based servers for the purpose of monitoring, using this module, the maintenance teams are able to monitor machine status from any place, which makes it possible to respond to the alerts in the system in a timely manner and also capitalize on the facts available during the decision making process.
5. The machine as claimed in claim 1, wherein a touch display is one of the components of the Solar Gateway Node that offers visualization of machine health and a graphical interface for instant interaction, these displays make it easy for operators to determine the system status, be notified of any alarms and potential maintenance requirements, and act immediately increasing interaction with the user and efficiency in monitoring the system.
6. The machine as claimed in claim 1, wherein a solar power supply has been embedded with every node to provide a durable and reliable off grid power solution that can be used in energy-deprived regions and keep the system running, such a source of power also greatly lowers the need for external mains, thus increasing reliability of the system and compatibility for various industrial environments.
7. The machine as claimed in claim 1, wherein a wireless communication module, that was included in the Data Collection and Routing Nodes, allows for low power data transmission even at close distances, by using this module, real-time transmission of sensor measurements captured from machines to intermediate nodes is possible, which allows a smooth tracking of the machine's operating status and data of all types.
8. The machine as claimed in claim 1, wherein the Long-Range RF Communication Module, installed in the Routing Node and SolarGateway Node, has enhanced data transmission over larger industrial areas, this module enables the integration of data collected from distant nodes into the central system which aids in effective supervision over large distances while providing the required connection within the predictive maintenance system.

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