DIAGNOSTICS, REPAIR RECOMMENDATIONS AND PREDICTIVE MAINTENANCE FOR VEHICLES BASED ON OBD II USING FOG COMPUTING

Abstract

ABSTRACT Diagnostics, Repair Recommendations and Predictive Maintenance for Vehicles based on OBD II using Fog Computing The present disclosure introduces a diagnostics, repair recommendation, and predictive maintenance system for vehicles, utilizing OBD II port 102 to access data from the vehicle's ECU 122. The system captures DTC codes 104 and real-time diagnostic data through elm 327 scanner 106, transmitting it to edge processors 108 which execute real time diagnostic algorithm 124 for immediate fault detection. Advanced data analysis is performed by fog node 110, which employs machine learning module 112 to identify patterns and predict maintenance needs based on historical and real-time data. Data communication module 114 enables efficient transfer of critical insights to cloud platform 116 for long-term storage and trend analysis. The user interface 118 displays diagnostic results and maintenance suggestions, while the alert system 120 provides real-time notifications of critical issues to vehicle owners and workshops, enhancing proactive vehicle health management and safety. Reference Fig 1

Specification

Description:DETAILED DESCRIPTION

[00025] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00026] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00027] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00028] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00029] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00030] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00031] Referring to Fig. 1 and Fig. 2, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of OBD II port 102, DTC code 104, ELM 327 scanner 106, edge processors 108, fog node 110, machine learning module 112, data communication module 114, cloud platform 116, user interface 118, alert system 120, ECU 122, real time diagnostic algorithm 124 and PID command 126.

[00032] Referring to Fig. 1 and Fig. 2, the present disclosure provides details of diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100. It is designed to enable real-time diagnostics, anomaly detection, and proactive maintenance by processing data locally through edge and fog nodes. In one embodiment, the system comprises key components such as OBD II port 102, DTC code 104, ELM 327 scanner 106, edge processors 108, and fog node 110, which collectively facilitate efficient data gathering and analysis. The system also incorporates machine learning module 112 and real-time diagnostic algorithm 124 to enhance predictive capabilities and provide tailored repair recommendations. Additional component are data communication module 114, cloud platform 116, user interface 118, and alert system 120, enable seamless communication, data storage, and user notifications. The ECU 122 and PID command 126 provide critical diagnostic data, making the system a comprehensive solution for vehicle health management and preventive maintenance.

[00033] Referring to Fig. 1 and Fig. 2, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with OBD II port 102, which acts as the gateway to retrieve data from the vehicle's electronic control units (ECUs) for real-time diagnostics. The OBD II port 102 connects to the ELM 327 scanner 106 to capture diagnostic trouble codes (DTCs) 104 and other key vehicle metrics. It facilitates seamless data flow from the vehicle to edge processors 108 for further processing and real-time issue detection. This data is also forwarded to the fog node 110 for deeper analysis, where machine learning module 112 interprets patterns for predictive insights. The OBD II port 102 is crucial in linking the vehicle's core diagnostic system with advanced edge and fog computing nodes.

[00034] Referring to Fig. 1 and Fig. 2, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with DTC code 104, which are error codes generated by the ECU 122 to signal specific issues within the vehicle. The DTC code 104 is retrieved via OBD II port 102 and analyzed by edge processors 108 to diagnose immediate issues. It provides essential data inputs to the machine learning module 112, which detects patterns and predicts maintenance needs. This code is also accessible to the user interface 118 for transparency and enables the alert system 120 to notify vehicle owners or workshops about potential malfunctions. The DTC code 104 thus forms the basis of both real-time diagnostics and predictive maintenance.

[00035] Referring to Fig. 1, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with ELM 327 scanner 106, which connects directly to OBD II port 102 to retrieve live vehicle data, including DTC codes 104. The ELM 327 scanner 106 transmits this data to edge processors 108 for initial diagnostics and fog node 110 for further analysis. It ensures seamless data capture from various ECUs 122 and sends essential parameters via PID command 126 to edge and fog layers. The scanner also supports data sharing with the cloud platform 116 for historical analysis and long-term trends. The ELM 327 scanner 106 serves as the main data extraction tool, linking the vehicle's internal systems with the diagnostic framework.

[00036] Referring to Fig. 2, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with edge processors 108, which handle real-time processing of vehicle data received from the ELM 327 scanner 106 through OBD II port 102. The edge processors 108 analyze DTC codes 104 and run the real time diagnostic algorithm 124 to detect any immediate issues, reducing latency and bandwidth usage by minimizing data sent to the cloud. These processors also work in tandem with fog node 110 to offload more complex analysis, enabling localized diagnostics. Edge processors 108 play a pivotal role in delivering real-time insights and optimizing system performance by managing data flow to the fog and cloud layers.

[00037] Referring to Fig. 1 and Fig. 3, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with fog node 110, which consolidates data from multiple edge processors 108 to perform deeper analysis and predictive maintenance tasks. The fog node 110 utilizes machine learning module 112 to identify potential issues before they become critical, enhancing the system's predictive capabilities. It processes DTC codes 104 and other vehicle data to provide tailored repair recommendations and maintenance schedules. Fog node 110 communicates with cloud platform 116 for further data storage and model updates, ensuring an adaptive diagnostic system. This layer bridges the local edge processing and the global data insights in the cloud.

[00038] Referring to Fig. 1, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with machine learning module 112, which applies data-driven models to enhance diagnostic accuracy and predict vehicle maintenance needs. The machine learning module 112 operates within fog node 110, analyzing patterns in DTC codes 104 and other real-time data from edge processors 108. This module continuously learns from historical data stored in cloud platform 116 to improve predictions and provide more effective maintenance advice. By leveraging large datasets and machine learning techniques, machine learning module 112 enables proactive vehicle health management.

[00039] Referring to Fig. 1, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with data communication module 114, which enables secure and efficient data exchange between OBD II port 102, edge processors 108, fog node 110, and cloud platform 116. This module ensures real-time transmission of DTC codes 104 and other diagnostic data, allowing for fast and reliable processing at each stage. The data communication module 114 also supports connectivity with the user interface 118 and alert system 120, ensuring users receive timely notifications about their vehicle's health. By managing data flow, this module underpins the entire diagnostic and maintenance system.

[00040] Referring to Fig. 1 and Fig. 3, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with cloud platform 116, which stores critical vehicle data and predictive models used to improve diagnostics over time. The cloud platform 116 receives data from fog node 110 and edge processors 108, archiving long-term trends in DTC codes 104 and maintenance records. It continuously updates the machine learning module 112 with new insights, improving the accuracy and reliability of predictions. The cloud platform 116 also provides historical data access for workshops, allowing a comprehensive view of the vehicle's diagnostic history.

[00041] Referring to Fig. 3, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with user interface 118, which allows vehicle owners and workshops to access diagnostic information, real-time alerts, and maintenance recommendations. The user interface 118 displays data from edge processors 108 and fog node 110, helping users understand DTC codes 104 and diagnostic results. It is also connected to alert system 120, delivering timely notifications about any urgent issues detected. The user interface 118 ensures that users have accessible and transparent information on their vehicle's health.

[00042] Referring to Fig. 2, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with alert system 120, which sends notifications to vehicle owners and workshops when critical issues or maintenance needs are identified. The alert system 120 is triggered by insights from edge processors 108 and fog node 110, relaying DTC codes 104 and other diagnostic data through user interface 118. It plays a key role in preventing potential breakdowns by providing timely alerts for proactive maintenance. The alert system 120 enhances safety and responsiveness within the diagnostic framework.

[00043] Referring to Fig. 2, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with ECU 122, which is the main source of diagnostic data within the vehicle. The ECU 122 generates DTC codes 104 in response to any malfunctions and sends this data through OBD II port 102. Each ECU 122 manages a specific system ensuring that comprehensive vehicle diagnostics are available. Data from ECU 122 flows to edge processors 108 for initial analysis and to fog node 110 for predictive maintenance, enabling accurate and real-time fault detection.

[00044] Referring to Fig. 1 and Fig. 2, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with real time diagnostic algorithm 124, which processes DTC codes 104 and other immediate vehicle data to identify any malfunctions instantly. This algorithm is run on edge processors 108, enabling rapid diagnostics without latency associated with cloud processing. The real time diagnostic algorithm 124 works in conjunction with machine learning module 112 to provide immediate alerts through alert system 120 and supports real-time fault identification and decision-making.

[00045] Referring to Fig. 2, diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 is provided with PID command 126, which specifies the types of data requested from the ECU 122 through OBD II port 102. The PID command 126 retrieves data points like engine temperature, fuel levels, and specific DTC codes 104, enabling targeted diagnostics. This command structure allows edge processors 108 and fog node 110 to gather relevant data efficiently, facilitating quick and accurate diagnostics and maintenance predictions. The PID command 126 is essential for real-time monitoring and detailed analysis of vehicle performance.

[00046] Referring to Fig 4, there is illustrated method 200 for diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100. The method comprises:
At step 202, method 200 includes connecting the ELM 327 scanner 106 to the OBD II port 102 to retrieve data from the ECU 122;
At step 204, method 200 includes the elm 327 scanner 106 capturing DTC codes 104 and other diagnostic data from the ECU 122 and transmitting this data to edge processors 108;
At step 206, method 200 includes edge processors 108 running the real time diagnostic algorithm 124 on the captured DTC codes 104 and vehicle data to detect any immediate malfunctions;
At step 208, method 200 includes edge processors 108 sending the processed diagnostic data to fog node 110 for further analysis and predictive maintenance assessments;
At step 210, method 200 includes fog node 110 utilizing the machine learning module 112 to analyze historical and real-time data, identifying patterns and predicting future maintenance needs;
At step 212, method 200 includes the data communication module 114 transferring essential data from the fog node 110 to the cloud platform 116 for long-term storage, trend analysis, and model improvement;
At step 214, method 200 includes the cloud platform 116 updating machine learning module 112 in fog node 110 with new insights to enhance diagnostic accuracy and predictive capabilities;
At step 216, method 200 includes the user interface 118 displaying diagnostic results, DTC codes 104, and maintenance recommendations to the vehicle owner and workshop for easy understanding and transparency;
At step 218, method 200 includes the alert system 120 notifying the vehicle owner and workshop of any critical issues or maintenance requirements based on the data from edge processors 108 and fog node 110;
At step 220, method 200 includes sending targeted PID commands 126 from edge processors 108 to ECU 122 through the OBD II port 102 to request specific real-time data needed for diagnostics, ensuring efficient and detailed monitoring of vehicle health.

[00047] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00048] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00049] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. A diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 comprising of
OBD II port 102 to provide a connection point for retrieving vehicle data from the ECU;
DTC code 104 to indicate specific vehicle malfunctions for diagnostics;
ELM 327 scanner 106 to gather real-time data from OBD II port for transmission to processing units;
edge processors 108 to conduct initial analysis and diagnostics on vehicle data;
fog node 110 to perform advanced analysis and predictive maintenance assessment;
machine learning module 112 to identify patterns and predict maintenance needs;
data communication module 114 to facilitate secure data transfer between components;
cloud platform 116 to store historical data and support long-term analysis;
user interface 118 to display diagnostic results and recommendations to users;
alert system 120 to notify vehicle owners of critical issues and maintenance needs;
ECU 122 to generate diagnostic data and DTC codes within the vehicle;
real time diagnostic algorithm 124 to analyze data for immediate fault detection; and
PID command 126 to request specific data from the ecu for diagnostics.

2. The diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 as claimed in claim 1, wherein OBD II port 102 is configured to connect to the vehicle's ECU and facilitate real-time retrieval of diagnostic data, allowing seamless integration with downstream components for enhanced data access and processing.

3. The diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 as claimed in claim 1, wherein DTC code 104 generated by the ECU is configured to provide specific diagnostic trouble codes, enabling quick identification of vehicle malfunctions and informing subsequent diagnostic actions for accurate fault detection.

4. The diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 as claimed in claim 1, wherein elm 327 scanner 106 is configured to capture real-time diagnostic data from the OBD II port and transmit this data to edge processors, facilitating data flow from vehicle systems to enable immediate diagnostic processing.

5. The diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 as claimed in claim 1, wherein edge processors 108 are configured to process diagnostic data and run real time diagnostic algorithm for identifying vehicle malfunctions, providing rapid fault detection and minimizing latency for immediate response.

6. The diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 as claimed in claim 1, wherein fog node 110 is configured to aggregate data from edge processors and perform predictive analysis using machine learning techniques, enabling proactive identification of potential issues and facilitating maintenance planning based on historical patterns.

7. The diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 as claimed in claim 1, wherein machine learning module 112 is configured to analyze real-time and historical data to detect complex fault patterns and improve predictive maintenance recommendations, providing adaptive insights that enhance diagnostic accuracy.

8. The diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 as claimed in claim 1, wherein user interface 118 is configured to display diagnostic results, DTC codes, and tailored maintenance recommendations to vehicle owners, enabling transparency and empowering informed decision-making regarding vehicle maintenance.

9. The diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 as claimed in claim 1, wherein alert system 120 is configured to notify vehicle owners and service providers of critical issues and maintenance needs in real-time, enhancing vehicle safety by facilitating timely responses to detected faults and preventive maintenance actions

10. The diagnostics, repair recommendations and predictive maintenance for vehicles based on OBD II using fog computing 100 as claimed in claim 1, wherein method comprises of
ELM 327 scanner 106 connecting to the OBD II port 102 to retrieve data from the ECU 122;
ELM 327 scanner 106 capturing DTC codes 104 and other diagnostic data from the ECU 122 and transmitting this data to edge processors 108;
edge processors 108 running the real time diagnostic algorithm 124 on the captured DTC codes 104 and vehicle data to detect any immediate malfunctions;
edge processors 108 sending the processed diagnostic data to fog node 110 for further analysis and predictive maintenance assessments;
fog node 110 utilizing the machine learning module 112 to analyze historical and real-time data, identifying patterns and predicting future maintenance needs;
data communication module 114 transferring essential data from the fog node 110 to the cloud platform 116 for long-term storage, trend analysis, and model improvement;
cloud platform 116 updating machine learning module 112 in fog node 110 with new insights to enhance diagnostic accuracy and predictive capabilities;
user interface 118 displaying diagnostic results, DTC codes 104, and maintenance recommendations to the vehicle owner and workshop for easy understanding and transparency;
alert system 120 notifying the vehicle owner and workshop of any critical issues or maintenance requirements based on the data from edge processors 108 and fog node 110; and
sending targeted PID commands 126 from edge processors 108 to ECU 122 through the OBD II port 102 to request specific real-time data needed for diagnostics, ensuring efficient and detailed monitoring of vehicle health.

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