LORA AND XBEE-ENABLED VISION EDGE DEVICE FOR PREDICTIVE STATUS MONITORING OF OIL REFINERY DISPLACEMENT PUMPS WITH IOT TECHNOLOGY

Abstract

A lora and xbee-enabled vision edge device for predictive status monitoring of oil refinery displacement pumps with iot technology comprises EdVDc Node allows current parameters of critical equipment to be collected and transmitted wirelessly which facilitates real-time monitoring and predictive maintenance of displacement pumps in oil refineries, this enables the enhancement of operational efficiency, this node is equipped with a raspberry pi, camera module, XBee RF module, vibration sensor, current sensor and temperature sensor, and power supply the EdVDR Node is equipped with a Raspberry Pi, an XBee RF Module, and a LoRaWAN Module, LED Indicator and power supply and includes additional features such as data visualisation and long-range data transmission, this is useful when the sites or plants are in distress as it makes operational reliability consistent.

Specification

Description:FIELD OF THE INVENTION
This invention relates to lora and xbee-enabled vision edge device for predictive status monitoring of oil refinery displacement pumps with iot technology.
BACKGROUND OF THE INVENTION
This development showcases an innovative technology for predicting the failure of the oil refinery's displacement pumps using status tracking in real time. It involves a system of edge devices that can sense data, process data and communicate wirelessly to track vibrations, currents and heat in real-time. A strong interconnectivity of nodes throughout the system adopts sturdy communication protocols for the transfer of data between the nodes. All the data collected through the sensors are sent to a special cloud server where their AI and big data for maintenance prediction analysis based automated recommendation systems enhance effectiveness while averting breakdowns. The system's web-dashboard and display modules achieve the design objectives with ease, as the target users are able to manage the system from approved terminals when they want to use the real time or historical data. This approach improves the predicted end goal of the industrial facility operations.
This invention provides a solution to the problem of timely and efficient predictive maintenance of oil refinery displacement pumps because it is essential to industrial operations but is one of the most failure prone machines. Preventive maintenance techniques employ scheduled inspections or repairs after the breakdown occurs, which in turn leads to unavailability of the plant, expensive repairs and jeopardised safety. Further more, the absence of timeous monitoring and predictive analysis means that early signs of wear and tear, misalignments or over work tend to be hard to detect. This project addresses these shortcomings using an integrated approach with the advanced IoT technology to establish a continuous monitoring and predictive analysis for the purpose of intervention, management of risks, reduction of downtimes and more efficient use of resources.
US9488169B2: Disclosed is a system for allocating torque, supplied in one embodiment by an engine, to multiple variable and fixed displacement pumps coupled together in a series by coupling elements interspersed between the pumps. A controller is included which monitors the torque applied to each coupling element. The controller automatically varies displacement in one or more of the variable displacement pumps to prioritize and allocate torque to optimize available torque while keeping the torque applied to each coupling element within the torque limit for that particular coupling element.
RESEARCH GAP: The integration of LoRa and XBee-based edge devices with AI-driven predictive analytics for real-time monitoring and maintenance of oil refinery displacement pumps is the novelty of the system.
AU2016203015B2: The invention relates to a positive displacement pump, comprising a drive unit and a pump unit having several working chambers, several displacement elements, and at least three cylinders, preferably exactly three cylinders, wherein the pump unit is double-acting. The invention further relates to a piston diaphragm pump forming a positive displacement pump, wherein a diaphragm stroke is caused by means of working liquid present on the first side of a diaphragm and a medium to be pumped is conducted through a diaphragm chamber bounded by the second side of the diaphragm due to the diaphragm stroke, and the diaphragm stroke is caused at a diaphragm position different from a vertical position of the diaphragm.
RESEARCH GAP: The integration of LoRa and XBee-based edge devices with AI-driven predictive analytics for real-time monitoring and maintenance of oil refinery displacement pumps 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 system works as a single integrated technology that has the capability of monitoring and forecasting the working conditions of oil refinery displacement pumps. It relies on a coherent distributed structure of edge devices that gather real time measurements of critical parameters such as vibration, current, and temperature. Specialized sensors supervise those parameters, and the data is processed at the edge in a distributed manner to achieve low latency performance. The edge devices form a wireless network and communicate using different nodes of the network and a central server. The edge devices also collect data that is sent to a custom built cloud server over secure means. The server uses machine learning techniques to recognize the patterns in the data, the anomalies, and the failures that are likely to happen. Such approaches enable the maintenance of equipment without waiting for breakdown and consequently, maintenance time and costs are also lowered. AI based recommendations are also generated with the aim of improving the operation and performance of the equipment.
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 system works as a single integrated technology that has the capability of monitoring and forecasting the working conditions of oil refinery displacement pumps. It relies on a coherent distributed structure of edge devices that gather real time measurements of critical parameters such as vibration, current, and temperature. Specialized sensors supervise those parameters, and the data is processed at the edge in a distributed manner to achieve low latency performance. The edge devices form a wireless network and communicate using different nodes of the network and a central server. The edge devices also collect data that is sent to a custom built cloud server over secure means. The server uses machine learning techniques to recognize the patterns in the data, the anomalies, and the failures that are likely to happen. Such approaches enable the maintenance of equipment without waiting for breakdown and consequently, maintenance time and costs are also lowered. AI based recommendations are also generated with the aim of improving the operation and performance of the equipment.
For enabling operability, the system features an easy to use web dashboard and a module onscreen interface. With these interfaces, operators and authorized users can view live and archived data, analytic performance evaluations, and alerts or recommendations. The presence of wireless communication as well as cloud technology makes the system scalable and deployable in diverse industrial settings without much infrastructure. This advancement therefore signifies a major development towards better reliability, safety and effective management of industrial equipment.
BEST METHOD OF WORKING
The EdVDc Node allows current parameters of critical equipment to be collected and transmitted wirelessly which facilitates real-time monitoring and predictive maintenance of displacement pumps in oil refineries. This enables the enhancement of operational efficiency. This node is equipped with a raspberry pi, camera module, XBee RF module, vibration sensor, current sensor and temperature sensor, and power supply.
The EdVDR Node is equipped with a Raspberry Pi, an XBee RF Module, and a LoRaWAN Module, LED Indicator and power supply and includes additional features such as data visualisation and long-range data transmission. This is useful when the sites or plants are in distress as it makes operational reliability consistent.
The EdVDt Node incorporates a mugger pi, LoRaWAN module, GPRS modem, HMI display, and power supply and provides remote access and visualization of data from the center and allows operators or authorized persons to view live data and system predictions which improves access and decision making.
The communication over the wireless network is safe and assured by the XBee RF module which is fitted within the EdVDc and EdVDR Nodes which enhance reliable data transmission between the edge devices and monitoring system without hindrance.
As part of the EdVDt Node, the HMI Display has an interface that is easy to use for a site operator allowing him to visualize current data and system notifications and therefore control and respond to equipment state in a timely manner.
ADVANTAGES OF THE INVENTION
1. The system incorporates multipurpose sensors, such as vibration, current, and temperature sensors, for monitoring important parameters and being able to spot anomalies immediately to always keep an intended functionality.
2. As data gathered and analyzed through Raspberry Pi modules is embedded in XBee RF and LoRaWAN modules, it provides predictive analytics that helps anticipate and manage potential failures, thereby minimizing unanticipated outages and maintenance costs.
3. The combination of XBee and LoRaWAN modules facilitates reliable communication between nodes over long distances and expands the possible applications of wireless sensors in complex industrial environments.
4. The system's edge devices have a modular structure implemented with a GPRS modem and HMI display, and this feature gives the system optimal flexibility to support various sizes of the operation in diverse industrial scenarios.
5. The information can be transmitted to a designated cloud server via Wi-Fi, allowing this system to leverage machine learning based analysis to improve equipment use through informed decisions.
6. HMIs and the web dashboard are designed in a user-friendly manner and enable quick visualization of relevant data by the operator and other authorized users.
, Claims:1. A lora and xbee-enabled vision edge device for predictive status monitoring of oil refinery displacement pumps with iot technology comprises EdVDc Node allows current parameters of critical equipment to be collected and transmitted wirelessly which facilitates real-time monitoring and predictive maintenance of displacement pumps in oil refineries, this enables the enhancement of operational efficiency, this node is equipped with a raspberry pi, camera module, XBee RF module, vibration sensor, current sensor and temperature sensor, and power supply.
2. The device as claimed in claim 1, wherein the EdVDR Node is equipped with a Raspberry Pi, an XBee RF Module, and a LoRaWAN Module, LED Indicator and power supply and includes additional features such as data visualisation and long-range data transmission, this is useful when the sites or plants are in distress as it makes operational reliability consistent.
3. The device as claimed in claim 1, wherein the EdVDt Node incorporates a mugger pi, LoRaWAN module, GPRS modem, HMI display, and power supply and provides remote access and visualization of data from the center and allows operators or authorized persons to view live data and system predictions which improves access and decision making.
4. The device as claimed in claim 1, wherein the communication over the wireless network is safe and assured by the XBee RF module which is fitted within the EdVDc and EdVDR Nodes which enhance reliable data transmission between the edge devices and monitoring system without hindrance.
5. The device as claimed in claim 1, wherein as part of the EdVDt Node, the HMI Display has an interface that is easy to use for a site operator allowing him to visualize current data and system notifications and therefore control and respond to equipment state in a timely manner.

Documents