Artificial intelligence-based chain level adjuster for two-wheelers

Abstract

An artificial intelligence-based chain level controller (10) for two-wheelers, comprising: a camera (15) which records one or more realistic parameters by means of image or video signals in order to detect changes in the chain condition, these parameters comprising, for example: chain slack, stretching vibrations or the like; an AI module (20) that receives the information acquired from the camera and compares / executes the operations for automatically adjusting the chain level based on the predefined instructions, taking into account the real-time conditions; a chain tensioner (40) positioned at the rear of the two-wheeler and used to adjust the chain level by means of spring adjustment, wherein the adjustment of the spring (30) can be controlled by the spring regulator (35), wherein the spring regulator is configured to adjust / control the height of the chain tensioner (40) from the reference position.

Description

TECHNICAL FIELD

[0001] The present invention relates to vehicle condition monitoring systems and techniques. More specifically, the present invention relates to an artificial intelligence-based chain leveling system for two-wheelers to prevent accidents caused by chain deformation. BACKGROUND

[0002] The chain is a critical component that transmits rotational motion from one vehicle part to another. In particular, it transmits engine rotation to the rear wheels. Proper chain tension contributes to efficient rotational transmission. However, the continuous movement of the chain over the teeth alters the chain tension due to material changes. Sagging or excessive chain tension over the vehicle's external components can lead to accidents if the vehicle is not used optimally. There is no specialized system on the market that utilizes AI techniques to continuously monitor and automatically adjust chain tension. Therefore, there is a need for an automatic chain tensioning system based on predefined instructions. The present invention effectively overcomes the above-mentioned problems, limitations, and disadvantages. OBJECT OF THE INVENTION

[0003] The main objective of the present invention is to provide a system for automatically adjusting chain tension without manual effort.

[0004] Another object of the present invention is to reduce accidents caused by slack or falling chains on vehicle exterior parts.

[0005] Another object of the present invention is to extend the service life of vehicle chains by properly monitoring the chain tension.

[0006] Another object of the present invention is to avoid manual checks and adjustments during chain tensioning.

[0007] Another aim of the present invention is to introduce artificial intelligence to avoid manual controls in operation.

[0008] Another object of the present invention is to provide an adjustable automatic chain tension adjustment system at an affordable price.

[0009] These and other objects and advantages of the present invention will become apparent from the following detailed description taken in conjunction with the accompanying drawings. SUMMARY

[0010] The present invention describes an artificial intelligence-based chain level adjustment system for two-wheelers. The chain level adjustment system comprises a camera that uses image or video signals to capture one or more realistic parameters to detect changes in the chain condition. These parameters can include, for example, chain slack, tension vibration, or the like. The AI ​​module receives the information captured by the camera and compares and executes the appropriate actions to automatically adjust the chain level based on predefined instructions and taking real-time conditions into account.

[0011] The chain tensioner is located on the rear of the bike. It regulates the chain height using a spring adjustment. The spring adjustment can be controlled using the spring adjuster. This adjusts the height of the chain tensioner from the reference position. The camera can be positioned at the top or bottom of the chain drive.

[0012] The AI ​​module controls the movement of the chain tensioner based on periodic comparison results from the camera. In one aspect, the microcontroller measures / calibrates the deviations using an AI module positioned in the respective vehicle. The chain tensioner is activated by the AI ​​module when a free-wheeling condition of the chain is detected.

[0013] These and other aspects of the embodiments described herein will become more fully understood in conjunction with the following description and the accompanying drawings. While the following descriptions show preferred embodiments and numerous specific details, they are illustrative and not limiting. Numerous changes and modifications are possible within the scope of the embodiments described herein without departing from the spirit thereof. The embodiments described herein are intended to include all such modifications. BRIEF DESCRIPTION OF THE DRAWING

[0014] The further objects, features and advantages will become apparent to those skilled in the art from the following description of the preferred embodiment and the accompanying drawings. Fig. shows the schematic diagram of the artificial intelligence-based chain level adjuster for two-wheelers according to an embodiment of the present invention.

[0015] The specific features of the present invention are shown in some drawings but not in others. This is for clarity only, as each feature may be combined with all or some of the other features according to the present invention. DETAILED DESCRIPTION

[0016] The various embodiments, as well as further developments and features, are explained in the following detailed description using non-limiting details. The depiction of processing techniques for known components is omitted in order not to unnecessarily obscure the embodiments described herein. The examples used herein are intended to facilitate understanding of the possible applications of the embodiments described herein and to enable those skilled in the art to implement the embodiments described herein. The examples are therefore not to be understood as limiting the scope of application of the embodiments described herein.

[0017] The various embodiments of the present invention disclose an artificial intelligence-based chain level adjustment system (10) for two-wheelers. The chain level adjustment system (10) comprises a camera, an AI module, and a chain tensioner. The camera (15) is configured to capture one or more realistic parameters using image or video signals to detect changes in the chain condition. These realistic parameters may include chain slack, stretching vibration, or the like.

[0018] The AI ​​module (20) receives the information captured by the camera and compares and executes the necessary steps for automatic chain adjustment based on predefined instructions, taking real-time conditions into account. The chain tensioner is located on the rear of the two-wheeler. The chain tensioner (40) adjusts the chain height using a spring adjustment. The spring adjustment can be controlled via the spring regulator.

[0019] Fig. schematically shows the artificial intelligence-based chain height adjuster for two-wheelers according to an embodiment of the present invention. The spring regulator (35) controls the height of the chain tensioner from the reference position. The camera can be positioned at the top or bottom of the chain drive. The AI ​​module (20) controls the movement of the chain tensioner based on periodic comparison results from the camera.

[0020] In one aspect, the microcontroller (25) measures / calibrates the deviations using an AI module positioned in the respective vehicle. The chain tensioner is controlled by the AI ​​module as soon as a freewheeling chain is detected.

[0021] The AI ​​module (20) serves as a decision support system and analyzes the information to determine the chain position using a camera. The camera is positioned on the vehicle and captures visual information. The microcontroller system is configured to enable optimal performance on different bicycles and under different conditions. According to one embodiment, the microcontroller (25) serves as the control component of the system by analyzing the information to control the spring regulator and serves as a safety system.

[0022] Adjusting the chain slack ensures smooth and consistent chain travel thanks to a spring mounted at the bottom, controlled by a spring regulator. Excessive chain slack can cause vibration and noise and prevent the chain from engaging properly with the sprocket, which in turn impairs smooth chain travel. The spring regulator counteracts the deflection force, keeping the indicator in one position.

[0023] The camera (15) captures visual information, including the bicycle's chain position. The integration of this system with an advanced AI module creates a robust technological solution that leverages real-time data on the bicycle's chain position. Real-time analysis of chain position scenarios is a key feature, enabling the AI ​​module to make informed decisions about adjusting the chain position. The placement enables an optimized coverage field and ensures process-dependent chain position. This targeted approach increases the overall effectiveness of the safety mechanism.

[0024] The integration of the device with an AI module (20) marks a departure from the traditional safety setup of the chain level. The AI ​​system / module serves as a decision-making authority and continuously analyzes the chain level to make adjustments and react in real time. The data used by the AI ​​system / module includes various parameters critical to the chain level. Through continuous monitoring, the AI ​​system can make precise decisions regarding the chain level. Another key parameter considered in the real-time analysis is distance.

[0025] The AI ​​module / system (20) evaluates the distance between the chains and determines the appropriate strength and chain level. This ensures that the security mechanism is tailored to the specific conditions of the chain level and provides a tailored response for optimal security. Chain-level behavior represents an additional layer of complexity integrated into the AI ​​system's analysis. By assessing chain behavior and excessive use, the system can anticipate potential risks and adjust the chain accordingly.

[0026] The microcontroller system (25) is configured to enable optimal performance in various industries and under varying conditions. According to one embodiment, the microcontroller acts as the main component for controlling the system by analyzing the information to control the other components and serves as a safety system. The microcontroller serves as a dynamic decision-making authority and constantly analyzes the chain status to react and adapt in real time. The process-based safety approach represents a significant advance in industrial technology and goes beyond passive safety measures to actively prevent accidents through the chain status.

[0027] The data used by the microcontroller includes various parameters of the adjustment process. By continuously monitoring and analyzing the chain position, the microcontroller can make process decisions regarding chain adjustment. The microcontroller acts as a control system by analyzing the information. The chain tensioner is a mechanical device that uses a spring to maintain the correct tension of the bicycle chain. The spring (30) is mounted inside the chain tensioner.

[0028] The user can adjust the chain position using the camera and the AI ​​module. This ensures that the chain is always correctly tensioned and prevents it from slipping, jumping, or coming off while riding. The sensors measure the chain position on the bike and provide precise, real-time chain position data.

[0029] The examples of the present invention described above are for illustrative purposes only. Although the present invention has been described using a specific example, numerous modifications are possible without significantly affecting the teachings and advantages of the subject matter described herein. Further substitutions, modifications, and changes are possible without departing from the spirit of the present solution. All features disclosed in this description (including the appended claims, the abstract, and the drawings) and / or all steps of the methods or processes disclosed therein may be combined in any way, except for combinations in which at least some of these features and / or steps are mutually exclusive.Although the embodiments described herein are described in terms of various specific embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described herein. List of reference symbols: 10 An artificial intelligence-based chain tensioner for two-wheelers 15 Camera 20 AI module 25 microcontrollers 30 spring 35 spring control 40 chain tensioners

Claims

[1] An artificial intelligence-based chain level controller (10) for two-wheelers, comprising: a camera (15) which records one or more realistic parameters by means of image or video signals in order to detect changes in the chain condition, these parameters comprising, for example: chain slack, stretching vibrations or the like; an AI module (20) that receives the information acquired from the camera and compares / executes the operations for automatically adjusting the chain level based on the predefined instructions, taking into account the real-time conditions; a chain tensioner (40) positioned at the rear of the two-wheeler and used to adjust the chain level by means of spring adjustment, wherein the adjustment of the spring (30) can be controlled by the spring regulator (35), wherein the spring regulator is configured to adjust / control the height of the chain tensioner (40) from the reference position. [2] The artificial intelligence-based chain level controller for two-wheelers according to claim 1, wherein the camera (15) can be positioned at the top or bottom of the chain drive. [3] The artificial intelligence-based chain level controller for two-wheelers according to claim 1, wherein the AI ​​module (20) controls the movement of the chain tensioner based on periodic comparison results received from the camera source. [4] The artificial intelligence-based chain level controller for two-wheelers according to claim 1, wherein the microcontroller (25) measures / calibrates the deviations using an AI module positioned in the respective vehicle. [5] The artificial intelligence-based chain level controller for two-wheelers according to claim 1, wherein the chain tensioner (40) is actuated by the AI ​​module as soon as an idle state of the chain is detected.

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