PREDICTIVE HEALTH MONITORING AND MACHINE LEARNING ANALYSIS OF TEXTILE SPUNBOND NONWOVEN MACHINES USING LORA PRIVATE NETWORK AND HTTPS SERVICES

Abstract

A predictive health monitoring and machine learning analysis of textile spunbond nonwoven machines using lora private network and https services comprises Data Collection Node has several modules for sensing vibration, temperature, and current and has a long-range communication module with an independent power supply; thus, the node is capable of monitoring the machine health parameters continuously in the textile nonwoven production processes, this node also makes extensive data collection possible and its relay to node and node, enabling early detection of faults and persistent surveillance of the state of the machines in the manufacturing processes data Relay Node includes a control board, long range communication modules, a secure node connectivity module and an independent power supply that increase the distances between the Data Collection Node and the central gateway, this node is useful in assisting data transmission and providing connectivity over long distance industrial areas making it and building premises which are habitable highly important.

Specification

Description:FIELD OF THE INVENTION
This invention relates to predictive health monitoring and machine learning analysis of textile spunbond nonwoven machines using lora private network and https services.
BACKGROUND OF THE INVENTION
This strategy allows for the delivery of an innovative solution that integrates machine conditions monitoring for textile spunbond nonwoven machines in real time over a secure LoRa network and HTTPS services. The entire structure includes several nodes that are joined together which receive basic signals, such as vibration, temperature and current readings and relay them to the cloud for subsequent machine learning analysis. Thanks to AI-powered analytics and forecasting features, operators are able to view alerts, performance metrics, and predictive alerts about the root cause of prospective problems right on a display. This preemptive approach cuts down on equipment inactivity, increases asset lifespan, and improves operational effectiveness by making effective and informed decisions based on data.
This invention addresses the pressing issues of machine breakdowns in spunbond nonwoven production due to a lack of adequate machinery maintenance. Such breakdowns are extremely costly since they lead to a loss in raw materials, periods of inactivity, and disturbances in supply chains. If you have a maintenance standard operating procedure (SOP) in place for maintenance actions, strictly within the bounds of a scheduled maintenance program, or implemented in response to a machine malfunction, then you have to understand, that this approach together with the SOP, is always extremely wasteful in terms of time and efforts. This system successfully uses machine learning and real-time data monitoring to identify when monitoring and machine usage should be performed. The deployment of this system also enhances the management of production processes, maintenance activities, and resource usage. This method decreases the cost of maintenance, makes the equipment more dependable, and enables non-stop production which is appropriate for high-efficiency manufacture scenarios.
KR100712363B1: Known methods include using a layer of particularly high absorbing fibers, such as wood pulp, on a nonwoven carrier and mixing the nonwoven composite with water to make it smaller. Disadvantages of this method are the purification process associated with the loss of high wood pulp fibers and circulating water for mixing equipment. According to the invention a fine layer of microfiber is used for the first time before it is used for wood pulp fibers. The microfibers are evenly distributed on the nonwoven carrier, for example using a meltblown process and the wood pulp fibers are used only in separate layers. During the mixing process, water no longer fuses wood pulp fibers into the nonwoven carrier. This is because the microfibers act as a barrier.
RESEARCH GAP: LoRa-based predictive health monitoring and machine learning analysis for textile spunbond nonwoven machines using HTTPS-secured cloud services is the novelty of this system.
US10745836B2: The presently disclosed subject matter relates to a multilayer nonwoven material. More particularly, the presently disclosed subject matter relates to multilayered structures including, but not limited to, two, three, or four layers to form the nonwoven material. The multilayered structure can include a first layer comprising continuous filaments and a second layer comprising bonded fibers. The continuous filaments can be synthetic filaments. The fibers can be cellulosic fibers, noncellulosic fibers, or combinations thereof. Certain layers can also contain a binder material.
RESEARCH GAP: LoRa-based predictive health monitoring and machine learning analysis for textile spunbond nonwoven machines using HTTPS-secured cloud services 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 innovation is a predictive health monitoring system designed for the operational health of spunbond nonwoven machines, integrating real-time data acquisition, private LoRa networking and HIPPA compliant cloud communication based on HTTPS. Production of textiles, especially in the textile spunbond non woven process, heavily relies on machine uptime to ensure that operations are cost-effective and production cycles are met. Nevertheless, standard forms of maintenance approach the problem of addressing failures after they have happened, routinely checking and fixing machinery, which ends up causing high aspects of further costs associated with loss of time, material or processes. This innovation suggests an answer by offering such predictive health monitoring which focuses on key parameters of machines that are subject to wear or failure and continually monitors them on a regular basis.
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 innovation is a predictive health monitoring system designed for the operational health of spunbond nonwoven machines, integrating real-time data acquisition, private LoRa networking and HIPPA compliant cloud communication based on HTTPS. Production of textiles, especially in the textile spunbond non woven process, heavily relies on machine uptime to ensure that operations are cost-effective and production cycles are met. Nevertheless, standard forms of maintenance approach the problem of addressing failures after they have happened, routinely checking and fixing machinery, which ends up causing high aspects of further costs associated with loss of time, material or processes. This innovation suggests an answer by offering such predictive health monitoring which focuses on key parameters of machines that are subject to wear or failure and continually monitors them on a regular basis.
The system consists of two main nodes: a data collection node and a data relay node. Collecting vibration, temperature, and current levels of the machine, this node is very essential for evaluating machine and operational status. Processing of data from these sensors is done at the site to avoid delays. This node transmits data to a relay node over long distances within the manufacturing facility utilizing LoRa RF technology. The relay node wirelessly connects with the data collection node allowing long distance wireless communication via LoRa and HTTPS to connect to the cloud. The advanced machine learning provides predictive solutions after the machine data is received safely at the cloud server. The operator is presented with the display interface which includes system analytics, system predictions and operational alerts in order to provide a visual impression of the machine health status. A notification system also provides audio alerts for pressing maintenance needs.
Data collected is transmitted securely to a custom cloud server, where it undergoes processing by machine learning models trained on historical data to identify potential machine issues. Predictive analytics within the cloud platform deliver actionable insights back to operators, allowing them to monitor trends, forecast failures, and optimize maintenance schedules. This cloud-based approach provides a centralized platform for long-term data storage and in-depth analysis, enhancing decision-making capabilities for predictive maintenance. This setup achieves high reliability, low latency, and scalability, making it suitable for a wide range of industrial conditions. With its combination of local processing, secure cloud connectivity, and predictive analytics, this system offers a comprehensive solution for monitoring and maintaining textile nonwoven machinery. The system is designed to be user-friendly, offering visual displays and notifications that allow operators to monitor machine health in real time. It eliminates guesswork by providing accurate, data-driven insights and maintenance alerts. This proactive approach helps reduce unexpected breakdowns and ensures a steady production flow. Additionally, the use of a private LoRa network and HTTPS communication enhances data security and enables remote monitoring, making it easy to scale and adapt to various manufacturing setups. The software is quite simple to use, having visual indicators and alerts that enable the staff to check the condition of the equipment at any given moment. It removes uncertainty by providing relevant, timely information and maintenance notifications based on data analysis. Such an approach contributes to minimizing unforeseen failures and maintaining a consistent production process. Also, the implementation of an owned LoRa network and HTTPS transmission improves data security and facilitates remote access, which makes it possible to extend and modify the system for different manufacturing environments easily.
BEST METHOD OF WORKING
The Data Collection Node has several modules for sensing vibration, temperature, and current and has a long-range communication module with an independent power supply; thus, the node is capable of monitoring the machine health parameters continuously in the textile nonwoven production processes. This node also makes extensive data collection possible and its relay to node and node, enabling early detection of faults and persistent surveillance of the state of the machines in the manufacturing processes.
Data Relay Node includes a control board, long range communication modules, a secure node connectivity module and an independent power supply that increase the distances between the Data Collection Node and the central gateway. This node is useful in assisting data transmission and providing connectivity over long distance industrial areas making it and building premises which are habitable highly important.
As for the Central Gateway Node it has a central processing unit along with a number of communication modules, visual display and loudspeakers and a source of power, this node is able to collect the information and display them showing the data analytic in real time. This enables timely alert and access to the state of machines operational for the operators thus enhancing the control and the response to events during the operations in the textile industry during production.
The LoRa RF Communication Module, used in both the Data Collection Node and the Data Relay Node, allows traceable data to be sent over a long distance with low power usage. This module aids nodes in communicating with one another and sending data from the source to the main system so that effective real time monitoring and machine condition assessment can be performed at great distances.
The Cloud Communication Module located inside the Central Gateway Node allows for safe remote communication to a proprietary cloud server. This module allows machine data to be collected from relatively remote operators and sent to them for analysis and prediction, ensuring cost effective long distance monitoring and providing better selection and resolution to the decision making process.
The touch's Interactive Display is contained in the Central Gateway Node and allows an operator to monitor in real time machine health and analytics pertaining to its maintenance activities. This display is critical as it allows some of the system features including the system status, alerts, and predictive information to be accessed directly to avoid the system from being complicated and enhance efficiency on the monitoring system.
ADVANTAGES OF THE INVENTION
1. Thanks to its real-time monitoring capability, the system allows for the early diagnosis of faults hence preventing unanticipated breakdowns and assuring uninterrupted processes.
2. The reduction of maintenance costs: The system eliminates the need to conduct regular inspections and undertake corrective measures, thereby reducing maintenance costs and saving resources.
3. Enhanced equipment functioning: Early detection of faults paves the way for use of predictive maintenance strategies to reduce the wear and tear of the equipment in use.
4. Optimized maintenance scheduling: Such cloud-hosted predictive analytics systems allow the operators to carry out maintenance scheduling which is an improvement on current operations.
5. Reliable and Cost-effective network: This allows for easy data transfer. The system is able to be taken up on numerous facilities or machines hence providing options for different industrial settings.
, Claims:1. A predictive health monitoring and machine learning analysis of textile spunbond nonwoven machines using lora private network and https services comprises Data Collection Node has several modules for sensing vibration, temperature, and current and has a long-range communication module with an independent power supply; thus, the node is capable of monitoring the machine health parameters continuously in the textile nonwoven production processes, this node also makes extensive data collection possible and its relay to node and node, enabling early detection of faults and persistent surveillance of the state of the machines in the manufacturing processes.
2. The machine as claimed in claim 1, wherein data Relay Node includes a control board, long range communication modules, a secure node connectivity module and an independent power supply that increase the distances between the Data Collection Node and the central gateway, this node is useful in assisting data transmission and providing connectivity over long distance industrial areas making it and building premises which are habitable highly important.
3. The machine as claimed in claim 1, wherein as for the Central Gateway Node it has a central processing unit along with a number of communication modules, visual display and loudspeakers and a source of power, this node is able to collect the information and display them showing the data analytic in real time, this enables timely alert and access to the state of machines operational for the operators thus enhancing the control and the response to events during the operations in the textile industry during production.
4. The machine as claimed in claim 1, wherein the LoRa RF Communication Module, used in both the Data Collection Node and the Data Relay Node, allows traceable data to be sent over a long distance with low power usage, this module aids nodes in communicating with one another and sending data from the source to the main system so that effective real time monitoring and machine condition assessment can be performed at great distances.
5. The machine as claimed in claim 1, wherein the Cloud Communication Module located inside the Central Gateway Node allows for safe remote communication to a proprietary cloud server, this module allows machine data to be collected from relatively remote operators and sent to them for analysis and prediction, ensuring cost effective long distance monitoring and providing better selection and resolution to the decision making process.
6. The machine as claimed in claim 1, wherein the touch's Interactive Display is contained in the Central Gateway Node and allows an operator to monitor in real time machine health and analytics pertaining to its maintenance activities, this display is critical as it allows some of the system features including the system status, alerts, and predictive information to be accessed directly to avoid the system from being complicated and enhance efficiency on the monitoring system.

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