PAN NETWORK AND AI-ENABLED PREDICTIVE HEALTH MONITORING OF HYDRAULIC OIL CONDITION IN AEROSPACE PORTAL MILLING MACHINES

Abstract

A system of pan network and ai-enabled predictive health monitoring of hydraulic oil condition in aerospace portal milling machines comprises AIPANTx Node (100) with Arduino Tiny Machine Learning Kit (110), XBee RF Module (120), accelerometer (130), current sensor (140), temperature sensor (150), pressure sensor (170) and power infrastructure is instrumental in acquiring data from aerospace portal milling machines in real time while transmission to the receiving node is done wirelessly and continuously for monitoring and maintenance purposes AIPANRx Node also features a Jetson Nano Board, XBee RF Module, GSM GPRS Modem, HMI Display, and power supply, which provides stronger functionality with the integration of such features like, strong data processing capabilities, real time notifications and provision of on site graphical interface which informs the relevant operators on dangerous conditions immediately.

Specification

Description:FIELD OF THE INVENTION
This invention relates to pan network and ai-enabled predictive health monitoring of hydraulic oil condition in aerospace portal milling machines.
BACKGROUND OF THE INVENTION
This invention develops a health management system for the condition of hydraulic oil in aerospace portal milling machines, utilizing the latest sensors and artificial intelligence technology. The system is composed of a distributed structure which consists of the sending node and the receiving node. The sending node gathers actual values of some parameters such as acceleration, current, temperature, and pressure, and sends it to receiving node wirelessly. The data collected is processed, and machine learning algorithms are applied to generate predictions followed by the recommended actions which are presented through an integrated human-machine interface or a web-based dashboard. The system is linked to the cloud server that was purpose-built for the monitoring, which allows operators and other authorized employees to make projections and retrieve information even when they are away from the site, guaranteeing pre-emptive repairs and improved operation of the machines.
Fuel failure is a phenomenon that causes oil losses, lowers machine efficiency, and leads to unwanted downtimes in aerospace portal milling machines. Actual monitoring practices tend to be passive and are characterized by periodic manual checks or simple alarms, which do not provide insight into potential failures beforehand or in the course of its operational state. This invention solves the problem of advancement by providing the capability to monitor certain parameters in real time and apply machine-learning algorithms for operational oil degradation and system functionality forecasting. As a result, the machinery owning and operating costs are decreased, productivity is improved, and the machinery reliability and durability are increased, which, in turn, minimizes work interruptions and brings down the operating maintenance costs.
CN103962602B: Patent of the present invention discloses a kind of numerical control multi-shaft gantry drilling and milling machine tool, including a gantry, is arranged below at described gantry and workpiece can be made at the X-direction of horizontal plane and a lathe bed of Y-direction motion;Described lathe bed is provided with the clamping device for clamping workpiece, the most hard-wired gantry frame in described lathe bed top is provided with gang drill, described gang drill includes multiple drilling-milling apparatus, described drilling-milling apparatus is driven its in the vertical direction to move up and down by two-stage power set, the two-stage realizing vertical range between drilling-milling apparatus and workpiece adjusts, and drilling-milling apparatus is driven it to rotate by same set of driving means.The present invention substantially increases high-volume and bores the operating efficiency of Milling Machining, reduces the gap with advanced country.
RESEARCH GAP: AI-enabled predictive health monitoring of hydraulic oil conditions in aerospace portal milling machines using a two-node architecture with machine learning and cloud analytics is the novelty of the system.
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: AI-enabled predictive health monitoring of hydraulic oil conditions in aerospace portal milling machines using a two-node architecture with machine learning and cloud analytics is the novelty of the 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.
The two-node architecture designed for the innovation employs monitoring and prediction techniques on the health of hydraulic oil used on the aerospace portal milling machine. The transmitting node is located in close vicinity to the machine with an aim of capturing real-time data relevant to the acceleration, current, temperature, and pressure measurement. These sensors also work continuously capturing changes in the operational behavior of the machine which may be witnessed as changes caused by time accumulation or potential hydraulic oil deterioration, or mechanical defect. The data collected is then sent wirelessly to the receiving node over a reliable communication protocol. Upon the data arrival at the node, the system uses some algorithms to process the incoming data seeking patterns and anomalies. This node also functions as the core processing unit that utilizes machine learning models to enable forecasting of the health of hydraulic oil and performance of the entire machine. Of the most importantly, all abnormalities have been marked and recommendations for actions have been provided. Such information can then be transmitted to an integrated human-machine interface and web dashboard for real time visualization by operators and authorized personnel.
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 two-node architecture designed for the innovation employs monitoring and prediction techniques on the health of hydraulic oil used on the aerospace portal milling machine. The transmitting node is located in close vicinity to the machine with an aim of capturing real-time data relevant to the acceleration, current, temperature, and pressure measurement. These sensors also work continuously capturing changes in the operational behavior of the machine which may be witnessed as changes caused by time accumulation or potential hydraulic oil deterioration, or mechanical defect. The data collected is then sent wirelessly to the receiving node over a reliable communication protocol. Upon the data arrival at the node, the system uses some algorithms to process the incoming data seeking patterns and anomalies. This node also functions as the core processing unit that utilizes machine learning models to enable forecasting of the health of hydraulic oil and performance of the entire machine. Of the most importantly, all abnormalities have been marked and recommendations for actions have been provided. Such information can then be transmitted to an integrated human-machine interface and web dashboard for real time visualization by operators and authorized personnel.
The system is also improved due to the use of a cloud server that stores all the collected information. The server allows for longitudinal studies and aids AI in the enhancement of predictive models. A user is able to remotely connect to the cloud and manage multiple machines from different locations. This ability to monitor and predict the activities at the same time guarantees advanced scheduling of maintenance activities, lowers chances of unplanned downtimes, and improves the effectiveness and reliability of the milling machines, thereby giving a big technological edge in the production of aerospace components.
BEST METHOD OF WORKING
The AIPANTx Node with Arduino Tiny Machine Learning Kit, XBee RF Module, accelerometer, current sensor, temperature sensor, pressure sensor and power infrastructure is instrumental in acquiring data from aerospace portal milling machines in real time while transmission to the receiving node is done wirelessly and continuously for monitoring and maintenance purposes.
The AIPANRx Node also features a Jetson Nano Board, XBee RF Module, GSM GPRS Modem, HMI Display, and power supply, which provides stronger functionality with the integration of such features like, strong data processing capabilities, real time notifications and provision of on site graphical interface which informs the relevant operators on dangerous conditions immediately.
There are several XBee RF Modules that occupy the two nodes, AIPANTx and AIPANRX. This makes the connection uninterrupted and reliable which are the key elements when communicating without wires.
For example, the AIPANRx Node uses GSM GPRS Modem which allows the custom cloud server to connect to the internet which would broaden the capabilities of any operator or authorized personnel by allowing them to check AI predictive analytics as well as monitoring data thereby enhancing decision making and communication efficiency.
Senior on-site decision-makers will benefit from the user friendly interface of the HMI Display that is embedded in AIPANRx Node and is capable of showing real-time insights and recommendations based on ML analytics for improved situational awareness and maintenance effectiveness.
ADVANTAGES OF THE INVENTION
1. The accelerometer, current, and temperature and pressure sensors make it possible to supervise the hydraulic oil and machine parameters continuously and on real-time so that deviations can be recognized at once.
2. Learning algorithms of machine may be applied to forecast possible breakage employing the Arduin Tiny Machine Learning Kit and Jetson Nano Board in on-device processing hoping to minimize unscheduled downtimes and maintenance costs.
3. It is proposed and shown that XBee RF Modular can be used efficiently for wireless data transmission between AIPANTx and AIPANRx sensor nodes without the use of wires.
4. The system incorporates GSM GPRS Modem which enables data pushing to a further cloud server which permits access any time remotely via a web interface and HMI display of the history and present situation.
5. Analytics and recommendations generated with AI enhance the role of the operators by providing them with insights that can be acted upon. This in turn enhances the efficiency of the machines, and critical parts have their operational lifespan lengthened.
6. The modular design based on Jetson Nano and Arduino Tiny Machine Learning Kit is easily expandable and can also be adapted for different industrial machines which makes the solution useful in many applications.
, C , Claims:1. A system of pan network and ai-enabled predictive health monitoring of hydraulic oil condition in aerospace portal milling machines comprises AIPANTx Node (100) with Arduino Tiny Machine Learning Kit (110), XBee RF Module (120), accelerometer (130), current sensor (140), temperature sensor (150), pressure sensor (170) and power infrastructure is instrumental in acquiring data from aerospace portal milling machines in real time while transmission to the receiving node is done wirelessly and continuously for monitoring and maintenance purposes.
2. The system as claimed in claim 1, wherein the AIPANRx Node also features a Jetson Nano Board, XBee RF Module, GSM GPRS Modem, HMI Display, and power supply, which provides stronger functionality with the integration of such features like, strong data processing capabilities, real time notifications and provision of on site graphical interface which informs the relevant operators on dangerous conditions immediately.
3. The system as claimed in claim 1, wherein there are several XBee RF Modules that occupy the two nodes, AIPANTx and AIPANRX, this makes the connection uninterrupted and reliable which are the key elements when communicating without wires.
4. The system as claimed in claim 1, wherein for example, the AIPANRx Node uses GSM GPRS Modem which allows the custom cloud server to connect to the internet which would broaden the capabilities of any operator or authorized personnel by allowing them to check AI predictive analytics as well as monitoring data thereby enhancing decision making and communication efficiency.
5. The system as claimed in claim 1, wherein senior on-site decision-makers will benefit from the user friendly interface of the HMI Display that is embedded in AIPANRx Node and is capable of showing real-time insights and recommendations based on ML analytics for improved situational awareness and maintenance effectiveness.

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