Description:
FIELD OF THE INVENTION

[0001] The present invention relates to the field of embedded systems, artificial intelligence (AI), predictive maintenance, and distributed communication networks. More specifically, it relates to a modular, hardware-anchored diagnostic system integrated with an AI-driven neural processing unit (NPU), configured to detect, predict, and respond to anomalies in real time using ultra-low power LoRa (Long Range) wireless communication protocols.

[0002] The invention finds applications in multiple technical domains including, but not limited to, smart infrastructure, rural and urban healthcare, agriculture, industrial automation, consumer electronics, disaster resilience networks, transportation, smart cities, and future-ready technologies such as quantum-assisted diagnostics, 6G communication frameworks, and edge-AI IoT ecosystems.

[0003] The invention contributes significantly to resolving real-world challenges in predictive maintenance, hardware longevity, fault recovery, and autonomous health diagnostics in constrained connectivity environments, particularly in underserved geographies like rural India and developing nations.

[0004] The invention also pertains to secure embedded control systems, tamper-resistant device networks, and future-forward mesh architectures incorporating hybrid AI communication for fault diagnostics and distributed learning across heterogeneous environments.

[0005] The present invention is directed toward real-time, hardware-integrated predictive maintenance using AI-driven processing units and LoRa-based mesh communication, particularly optimized for operation in network-scarce, resource-constrained environments such as rural India and disaster-prone infrastructure.
[0006] The invention is particularly optimized for deployment in infrastructure-deficient environments, including disaster-affected regions, remote medical facilities, smart agriculture, and mobile industrial platforms.

BACKGROUND OF THE INVENTION

[0007] Conventional diagnostic systems in electronic devices typically depend on reactive maintenance or cloud-driven predictive analytics. However, these approaches often suffer from major drawbacks—they're either too slow to respond in real time, consume excessive power, or rely heavily on uninterrupted high-speed internet connectivity, making them impractical for resource-constrained environments. These systems often lack real-time fault isolation, have minimal intelligence at the edge, and are cost-prohibitive for large-scale deployment, especially in resource-constrained geographies.

[0008] Most existing AI-based health monitoring systems suffer from significant limitations—they either operate as cloud-first platforms reliant on continuous internet connectivity (such as those built on Google or AWS IoT), require high-bandwidth communication channels like 4G/5G or Wi-Fi, provide only rule-based reactive maintenance lacking intelligent decision-making, or are confined within closed proprietary ecosystems such as Apple’s iOS diagnostics.

[0009] Furthermore, LoRaWAN networks operate in a star topology dependent on a gateway, lacking peer-to-peer resilience or collaborative fault reporting.

[0010] There exists a critical unmet need for a low-power, embedded diagnostic system capable of performing on-device predictive maintenance without relying on continuous cloud connectivity. Existing solutions often fail in rural, off-grid, or disaster-affected environments where access to cloud infrastructure is unavailable or unreliable. Additionally, conventional systems lack the ability to facilitate decentralized, cross-device communication necessary for cooperative diagnostics in dynamic mesh topologies. Integrating self-healing mechanisms into legacy equipment also remains a technical challenge, especially when such devices lack the hardware interfaces or computational capability for autonomous recovery. Most current approaches are heavily dependent on centralized servers for updates and health data analysis, which introduces latency, single points of failure, and logistical overhead.

[0011] India’s rural regions and emerging economies often lack robust network infrastructure. Most industrial devices remain unconnected or incapable of intelligent self-diagnostics. High equipment replacement costs and delayed maintenance contribute to loss of productivity and e-waste.

[0012] Globally, increasing demand for circular economy solutions, edge AI, sustainable design, and disaster-resilient systems demand a new class of devices that are autonomous, intelligent, decentralized, and energy-efficient.

[0013] Additionally, existing diagnostic systems lack the ability to self-personalize their behavior using device-specific historical data, do not perform real-time sensor fusion for anomaly detection, and are vulnerable to tampering or false data injection due to the absence of secure firmware-level controls. Furthermore, most existing LoRa-based systems rely on fixed topologies and cannot dynamically reconfigure based on peer loss, limiting their utility in public infrastructure, disaster-prone regions, and high-availability industrial environments.

[0014] While cloud-based platforms like AWS Greengrass and Google Cloud IoT offer edge analytics capabilities, they depend on reliable internet connectivity and often require token validation through central servers. This limits their use in environments where connectivity is intermittent or altogether absent. Additionally, closed diagnostic systems such as Apple’s proprietary repair architecture do not support cross-device communication or third-party integration, making them unsuitable for interoperable and decentralized diagnostic networks.

[0015] Unlike conventional systems reliant on cloud-first analytics (e.g., Google IoT, AWS Greengrass) or proprietary repair ecosystems (e.g., Apple Diagnostics), the disclosed invention decentralizes intelligence by anchoring diagnostics in edge hardware with mesh-based fault propagation. This removes dependency on continuous internet access, reducing latency and improving autonomy.

SUMMARY OF THE INVENTION

[0016] The invention discloses a hardware-integrated, AI-powered system and method for predictive maintenance and self-healing of electronic and electromechanical devices through LoRa-based ultra-narrowband wireless communication. Key features include a modular diagnostic unit incorporating a neural processing unit (NPU), diagnostic sensors, and embedded firmware; a custom anomaly compression and transmission protocol (LoDiag); a device-to-device (P2P) communication mesh that operates independently of traditional LoRaWAN gateways; a context-aware remediation logic engine capable of initiating automated or guided fault resolution actions; and a retrofit-ready architecture designed for seamless integration with legacy or non-smart devices.

[0016A] The system performs health monitoring, detects anomalies using machine learning models (e.g., LSTM, autoencoders), transmits compressed alerts over LoRa, and performs mitigation actions—without reliance on cloud servers or mobile networks.
It achieves real-time, low-power diagnostics and fault management with applicability across industries such as smart agriculture, e-mobility, healthcare, logistics, and infrastructure.

[0017] The invention provides a technical solution to a technical problem by embedding AI models into edge hardware, enabling device longevity, operational continuity, and predictive maintenance—thus avoiding exclusions under Section 3(k) of the Indian Patent Act.

[0018] In some embodiments, the invention further comprises a secure anomaly communication protocol (LoDiag+) that enables prioritized, compressed, and encrypted communication of diagnostic summaries based on fault severity and device context. Additionally, self-personalizing AI models, adaptive routing within the mesh, and tamper detection mechanisms enhance resilience. A retrofit version enables integration with legacy industrial systems, and cloud-agnostic gateways allow interfacing with third-party platforms without vendor lock-in.

[0019] The invention achieves a technical solution to the technical problem of the inability to detect or respond to device faults in environments without consistent network access or external cloud processing. By combining embedded AI, real-time diagnostics, and mesh-based fault reporting via LoRa, the invention enables resilient, scalable, and power-efficient self-healing systems for diverse applications.

[0020] Unlike conventional cloud-dependent diagnostic approaches, the present invention executes AI-driven anomaly detection and mitigation locally within a hardware-embedded module. It introduces a hardware-software hybrid capable of peer-to-peer coordination through LoRa, eliminating reliance on centralized data centers. The invention combines adaptive thresholding, decentralized event propagation, and fault isolation—delivering a resilient, scalable, and practical solution for remote diagnostics.

[0021] The present invention introduces a novel integration of machine learning, embedded hardware diagnostics, ultra-low-power LoRa communication, and autonomous fault mitigation within a compact module designed for constrained or infrastructure-scarce environments. While prior solutions in the domain of predictive maintenance rely heavily on cloud-based analytics platforms (such as Google Cloud AI or AWS Greengrass), or utilize proprietary rule-based diagnostic tools confined to single-vendor ecosystems (e.g., Apple’s internal service diagnostics), the disclosed system decentralizes intelligence by executing anomaly detection locally on a neural processing unit (NPU) integrated within the device itself.

[0022] A key inventive aspect lies in the module’s ability to not only identify faults autonomously but also propagate summarized diagnostic information through a peer-to-peer LoRa mesh network—without reliance on a central gateway or cloud infrastructure. Existing LoRaWAN architectures predominantly operate in centralized, hub-and-spoke topologies and do not natively support decentralized anomaly dissemination or mesh-based fault coordination. The present system departs from such conventions by enabling multi-hop diagnostic sharing and context-aware response logic directly between devices, thereby reducing latency, enhancing fault resilience, and ensuring continuity of service during network disruptions.

[0023] This architectural departure from cloud-dependence, combined with hardware-rooted AI inference, personalized adaptive thresholds, and context-sensitive actuation, collectively forms a non-obvious and inventive improvement over known systems. The invention solves long-standing limitations in predictive diagnostics for remote, mobile, and infrastructure-limited environments in a technically novel and industrially applicable manner.

[0024] The technical contributions described herein are embodied through concrete hardware interactions and real-time signal processing circuits, rather than abstract algorithms or software per se.

BRIEF DESCRIPTION OF DRAWINGS

[0025] FIG. 1 – It Illustrates the core internal components of the diagnostic module, including the neural processing unit (NPU), multi-sensor interface, LoRa transceiver, and embedded control logic. This architecture forms the foundation for predictive fault detection and communication. FIG. 1 is also submitted as Future of Abstract.

[0026] FIG. 2 – It illustrates the real-time processing flow: sensor data collection, AI-based anomaly detection, threshold validation, local mitigation action (e.g., throttling or alert), and LoRa message generation. LoDiag+ packet structure and prioritization logic are embedded within this flow.

[0027] FIG. 3 – It demonstrates how multiple diagnostic modules interact in a decentralized LoRa mesh network. Includes peer-to-peer routing, fallback paths in case of node failure, and multi-hop message propagation with prioritization.

[0028] FIG. 4 – It depicts a retrofit module connected to a legacy electronic device, integrating sensors and LoRa output. Also shows the secure tamper detection circuit, fallback logic activation, and isolation response to physical compromise.

[0029] FIG. 5 – Composite schematic illustrating use cases across rural vaccine cold-chain monitoring, smart grid fault diagnostics, and urban e-mobility fleet monitoring via LoRa-connected gateways.

[0030] FIG. 6 – It illustrates flowchart detailing how device-specific anomaly patterns, environmental inputs, and usage history feed into adaptive threshold adjustment and localized retraining of the machine learning model over time.

[0031] FIG. 7 – Operational flowchart showing the system’s fallback diagnostic and peer-to-peer communication protocol in the event of network or gateway failure.

[0032] The invention departs from traditional LoRaWAN’s centralized star-topology by introducing a resilient, self-healing mesh network that enables device-to-device relaying, local fallback behavior, and dynamic routing. Each node maintains an adaptive routing table updated in real time using physical-layer parameters such as Received Signal Strength Indicator (RSSI), peer availability, historical node reliability, and current energy status. In the event of a gateway or peer device failure, the mesh architecture autonomously reroutes diagnostic packets and emergency signals through alternate paths, without requiring manual reconfiguration or central intervention. This autonomous reconfiguration significantly enhances fault tolerance, reduces dependency on fixed infrastructure, and is particularly effective in disaster-prone regions, underground installations, and rural areas with unstable connectivity.

[0033] Furthermore, periodic sharing of localized health summaries among devices enables early detection of systemic anomalies, such as overheating patterns across a sub-region of solar inverters or vibration faults in a fleet of mobile assets. This shared intelligence forms the foundation for collaborative fault prediction and localized mitigation, representing a marked advancement over isolated or cloud-dependent diagnostics.

DETAILED DESCRIPTION OF THE INVENTION

[0034] The present invention relates to a system and method for predictive maintenance and self-healing of electronic and electromechanical devices via embedded AI processors and LoRa-based communication. The system comprises a hardware module that may be embedded during manufacture or retrofitted into an existing device. The module contains a neural processing unit (NPU), diagnostic sensors, a logic controller, and a LoRa transceiver.

System Architecture

[0035] FIG. 1 illustrates the core architecture of the diagnostic module, wherein the central component is a Neural Processing Unit (NPU), implemented using a low-power RISC-V core with embedded AI acceleration capabilities. This NPU interfaces with a Diagnostic Sensor Suite, comprising voltage and current sensors, thermal sensors for temperature profiling, vibration and motion sensors such as MEMS accelerometers, and optional environmental sensors for parameters like humidity, dust levels, and corrosion exposure.

[0036] A LoRa Transceiver, such as Semtech’s SX1262, is integrated to enable long-range, low-power communication. The transceiver supports both LoRaWAN and custom P2P (point-to-point) modes. The device includes a Memory Unit for logging anomalies, firmware updates, and machine learning model weights.

[0037] The LoDiag+ protocol stack operates on top of the LoRa MAC layer. It defines a lightweight diagnostic packet structure that includes 5 bytes of routing metadata, 2 bytes of priority flags, and a 16–32 byte compressed payload. Packets are signed using SHA-based message digests. The protocol supports chained packet encoding for multi-alert bundling, and features a retry throttling mechanism to reduce radio collisions in dense mesh deployments.

[0038] In one embodiment, the system utilizes a diagnostic communication protocol referred to as “LoDiag+,” specifically optimized for low-bandwidth and low-power environments. Each transmitted message comprises a compressed diagnostic summary, including elements such as device ID, anomaly type, timestamp, confidence score, and priority flag. Message sizes are deliberately restricted to under 50 bytes to allow rapid transmission via LoRa and ensure compatibility with mesh relaying protocols.

[0039] LoDiag+ supports both direct-to-gateway and multi-hop peer-to-peer routing modes, enabling robust communication even in the absence of cellular or broadband connectivity. It is further configured to propagate time-sensitive faults with higher precedence by leveraging embedded prioritization flags. This protocol plays a key role in maintaining synchronized health status across distributed devices while conserving energy and bandwidth.

[0040] The Control Logic and Power Manager manages wake-sleep cycles, event-triggered diagnostics, and energy optimization. Optional components include a Backup Power Source (e.g., coin cell or supercapacitor) for continued operation during primary failure.

[0041] FIG. 2 outlines the functional flow of the anomaly detection pipeline. The sensor data is continuously collected and preprocessed by the NPU. Using AI models (e.g., LSTM networks or shallow autoencoders), the module classifies operational behavior and detects deviations from baseline performance.

[0042] Upon detecting an anomaly, the device generates a compact diagnostic payload—typically under 30 bytes—using the custom LoDiag Protocol, which includes the device ID, fault code, confidence score, timestamp, and an optional suggested remediation tag.

[0043] If an anomaly is deemed non-critical, the device logs the event locally and includes it in the next scheduled transmission; however, for critical faults, it can initiate immediate actions such as throttling high-energy operations, activating redundant modules (like backup cooling or memory units), alerting peer devices or remote servers via LoRa, rebooting or recalibrating affected components, or entering a safe mode or full shutdown to prevent further damage.

[0044] As illustrated in FIG. 3, devices equipped with the invention form a collaborative diagnostic mesh by communicating directly with nearby units—unlike LoRaWAN’s traditional star topology—enabling peer-level awareness of device health, sharing of fault signature updates, coordinated responses such as load balancing, and redundant event reporting in case of gateway failure.

[0045] This decentralized mesh ensures that even if the central gateway is offline or unreachable (as in remote, disaster-struck, or underground facilities), health telemetry continues to be exchanged among devices.

[0046] FIG. 4 illustrates a retrofit version of the module housed in a standalone enclosure equipped with a USB-C or OBD-II interface, enabling seamless integration with legacy systems such as consumer appliances (e.g., refrigerators, televisions), industrial machines (e.g., motors, presses), vehicles (e.g., electric bikes, auto-rickshaws), and infrastructure elements like water pumps and solar inverters.

[0047] The retrofit device uses minimal signals (e.g., power line voltage, port-based thermal sensing) to generate meaningful fault predictions. This allows upgrading existing equipment without internal modification.

[0048] The invention accommodates multiple hardware configurations to suit diverse cost, space, and energy constraints. In one embodiment, the diagnostic module is embedded within a host PCB alongside other subsystems such as power management ICs or communication chipsets. In retrofit scenarios, the module is a standalone add-on unit that interfaces via GPIO, SPI, or UART. The core diagnostic engine may run on a dedicated Neural Processing Unit (NPU), or on a shared microcontroller (MCU) with AI acceleration, depending on processing needs. For space-constrained environments, such as wearables or drone payloads, the system can use low-profile MEMS sensors integrated with edge-optimized TinyML frameworks. The adaptability in hardware form factor and processing model supports vertical applications ranging from smart healthcare to defense-grade industrial monitoring.

Use Case Scenarios
[0049] FIG. 5: Rural Healthcare Device Monitoring
A vaccine fridge in a rural clinic is equipped with the diagnostic module. It detects compressor inefficiency via current fluctuation and temperature deviation. Before failure, it notifies the health worker and sends a compressed alert via LoRa to the district headquarters. A service call is auto-scheduled.

[0050] FIG. 6: Smart Grid Maintenance in Low-Connectivity Areas
Transformers in a rural microgrid have embedded modules. When a capacitor's temperature signature exceeds normal patterns, peer devices reroute load. Simultaneously, fault logs are stored locally and broadcast over LoRa to utility servers when the gateway reconnects.

[0051] FIG. 7: Urban Electric Scooter Network
Each e-scooter's module tracks battery health. On detecting swelling or charge retention anomalies, it initiates limited speed mode, notifies the driver via LED or mobile app, and shares the health status with nearby scooters and a maintenance dashboard.

[0052] A future version may hash each diagnostic event and log it to a lightweight distributed ledger to prevent log tampering or false warranty claims.

[0052A] To ensure future security, the system can employ post-quantum cryptographic algorithms (e.g., lattice-based or hash-based schemes) in encoding LoRa messages, particularly in healthcare or national infrastructure deployments.

[0053] In multi-device environments (e.g., agriculture sensors or autonomous drones), peer modules may collaboratively decide resource allocation or movement patterns using shared fault intelligence.

[0054] The invention provides a technical solution to the technical problem of detecting and responding to faults without reliance on cloud or network infrastructure, by employing hardware-anchored, AI-integrated modules that communicate via LoRa and execute embedded mitigation logic independently.

[0055] The claimed system does not constitute software per se or an abstract algorithm; rather, it is a tangible invention comprising concrete hardware components such as sensors, NPUs, memory, and LoRa transceivers, operating within a novel system architecture through an inventive LoDiag protocol and control logic physically executed within the device.

[0056] The invention is not directed to software per se or an abstract algorithm but is implemented through a tangible, physical system comprising discrete hardware components. These include a neural processing unit (NPU), sensor interfaces, memory blocks, and a LoRa communication module, all coordinated by embedded control logic. The diagnostic functions—such as anomaly detection, self-healing, data compression, and mesh routing—are executed directly within these hardware elements without requiring a general-purpose computer or external cloud processing. This hardware-rooted architecture ensures that the claimed invention delivers a demonstrable technical effect, such as decentralized fault detection and autonomous remediation in real-world environments.

[0057] The invention includes a lightweight diagnostic communication protocol, LoDiag+, configured to encode sensor anomalies into structured packets containing device ID, timestamp, anomaly class, fault score, and priority flags. This structure enables dynamic prioritization of messages based on severity, available bandwidth, and energy constraints, improving communication resilience.”

[0058] In one embodiment, sensor data is processed using real-time sensor fusion logic that combines thermal, voltage, and vibration profiles to improve anomaly detection reliability. A weighted algorithm assigns confidence levels to fused signals before triggering a diagnostic event. For example, if a temperature anomaly is detected on a rural inverter, but the vibration sensor shows normal operation, the neural engine applies a lower weight to the anomaly score. However, if both temperature and noise rise simultaneously, the combined risk exceeds the threshold and triggers local mitigation. These weights are automatically adjusted based on regional trends, seasonal factors, or historical context.

[0059] The embedded neural processing unit supports personalized learning, wherein anomaly detection thresholds and model parameters are periodically updated based on the device’s own operational history. This allows adaptation to environmental drift and component aging.

[0060] Over time, the neural engine adapts based on usage trends. For example, in one agricultural pump, vibration rise may consistently precede breakdowns, while in another it may not. The local model adjusts sensitivity on a per-device basis without global retraining, ensuring high specificity without cloud dependence.

[0061] A tamper detection circuit monitors for anomalies in voltage, casing breaches, or unauthorized debug port access. On detection, the module may disable diagnostics, trigger alerts, or encrypt logs to preserve data integrity.

[0062] The device includes a tamper-detection switch that, upon physical breach, logs the time and device state, disables external ports, and transmits a peer-alert flag if possible. The fallback controller performs a cryptographic integrity check on boot and resets to read-only mode until physical verification is performed.

[0063] Recognizing the sensitivity of health, industrial, or infrastructure data, the system adopts a multi-layered privacy architecture. All diagnostic payloads are anonymized at source using dynamic session IDs, without linking to personal identifiers unless explicitly enabled. LoRa packet payloads are encrypted using AES-128 or ChaCha20-Poly1305 depending on chipset availability. At the gateway level, payloads are verified for authenticity before cloud storage or dashboard relay. To comply with region-specific laws like India’s DPDP Act or GDPR in Europe, the system provides local consent caching and data retention configurability. The security logic is embedded at hardware abstraction level, ensuring protection irrespective of the OS or application stack.

[0064] In a peer-to-peer diagnostic network, if a node fails to respond, adjacent nodes dynamically reroute diagnostic data through alternate peers using adaptive routing tables. This ensures continued fault propagation paths even in partial mesh degradation scenarios.

[0065]The diagnostic firmware and AI model may be updated via encrypted OTA (over-the-air) delta updates. A dual-buffer update structure ensures rollback protection—if verification fails post-deployment, the device reverts to a stable prior image. Updates are validated via hash-check, hardware watchdog timers, and temperature-safe conditions before permanent flashing.

[0066] The system integrates an adaptive energy profiling mechanism that tracks power draw from individual subsystems, including sensor interfaces, radio modules, and AI inference engines. Based on available power (battery, solar, supercapacitor), it modulates operation—reducing sensor polling frequency, delaying non-urgent transmissions, or compressing data payloads. A sustainability logic layer logs energy consumption patterns, enabling predictive battery replacement or solar alignment recommendations. Such intelligence makes the system ideal for remote, unattended deployments where maintenance access is limited. In green tech applications, the system supports carbon-aware modes by syncing with renewable energy cycles or minimizing operation during high carbon-grid hours.

[0067] The invention includes an edge gateway module that translates diagnostic packets into standardized MQTT or REST API formats, enabling seamless integration with multiple cloud service providers or on-premise analytics tools.
When the battery level drops below critical threshold or in absence of sun for solar-powered modules, the device activates energy-aware minimization. It filters non-urgent anomalies, reduces packet size through entropy compression, and increases data sampling intervals—thus ensuring extended uptime under resource constraints.

[0068] The system architecture supports modular upgrades to its anomaly detection engine, including transformer-based models, federated learning units for collaborative training across peers, and quantized AI cores for ultra-low-power operations. All functions described herein are achievable by one skilled in the art, based solely on the enclosed schematic and logic descriptions, without requiring undue experimentation or non-public know-how.

[0069] The described system may be implemented using commercially available neural co-processors (e.g., RISC-V NPUs), MEMS-based sensor arrays, and LoRa-compatible microcontrollers. The AI logic can be trained using industry-standard open-source platforms and deployed within microcontroller firmware. No undisclosed proprietary elements are necessary to replicate the invention, ensuring enablement in accordance with the Indian Patents Act and global filing standards.

[0070] The system is architected to support a plug-in module ecosystem that enables functional extensibility without compromising core performance. These plug-ins may include third-party sensing modules (e.g., gas sensors, barometric pressure), AI co-processors, or encryption accelerators. Communication with such plug-ins is standardized via I²C, UART, or SPI interfaces using a lightweight command protocol stack that ensures secure handshaking and interrupt-driven task delegation.

[0071] This extensibility allows manufacturers or developers to tailor the diagnostic module to specific verticals (e.g., mining, aviation, marine equipment) without altering the base firmware. The control logic includes hot-swap detection to reconfigure runtime behavior dynamically, enabling field-serviced upgrades without device shutdown.

[0071A] The present invention is readily applicable across a wide range of industries, including but not limited to smart cities, public health infrastructure, industrial automation, fleet management, remote mining operations, and disaster-prone regions. Its low power design and self-healing mesh capability allow seamless integration in both urban and remote field conditions without requiring existing infrastructure or continuous internet access.

[0072] In another embodiment, the invention supports federated learning, where each diagnostic module trains its anomaly detection model locally and shares only encrypted gradient updates or weight differentials with peer nodes. This ensures data sovereignty and privacy compliance, particularly critical in healthcare or defense sectors.

[0073] The system includes a differential privacy engine that randomizes shared model deltas before mesh propagation. This minimizes the risk of information leakage while still enabling collaborative model improvement across the mesh network. Such architecture supports AI model evolution without centralized training or cloud upload, strengthening on-device learning without breaching sensitive environments.

[0074] The invention is designed to be application-layer agnostic, with an abstraction layer that allows integration with diverse domain-specific software stacks. Examples include SCADA systems in utilities, HL7/FHIR-based systems in healthcare, CAN-Bus interfaces in transport, and OPC-UA in manufacturing.

[0075] This application-level compatibility is achieved through a middleware layer that translates diagnostic output into domain-standard formats. This ensures that alerts or health summaries generated by the module can plug directly into industry dashboards without requiring vendor-specific middleware.

[0076] The diagnostic module is engineered for use in physically challenging environments. The hardware enclosure complies with IP65/IP67 ingress protection standards and is tested for resistance against shock, vibration (ISO 16750-3), and temperature cycling (MIL-STD-810H).

[0077] To ensure accuracy under duress, the invention incorporates an onboard fault emulation engine capable of injecting synthetic anomalies during scheduled self-checks. This enables real-time validation of the anomaly detection pipeline without relying on external calibration equipment. The system’s logic controller can emulate vibration surges, thermal drift, and voltage spikes to verify firmware responsiveness and AI reliability.

[0078] These concrete physical trigger conditions bind the software logic inseparably to the hardware architecture, ensuring that the claimed invention remains outside the scope of exclusions applicable to abstract software.

[0079] To avoid any interpretation that this invention is based purely on abstract algorithms or software running in isolation, all AI-related functions described in this disclosure are tied directly to hardware components. These may include dedicated neural processing units (NPUs), integrated AI accelerators within system-on-chip (SoC) designs, or custom-built application-specific integrated circuits (ASICs) optimized for low-power inference tasks.

[0080] The AI logic responsible for anomaly detection, self-adjustment, or predictive analytics is directly embedded in the hardware or executed in close conjunction with physical circuit blocks. This hardware anchoring guarantees technical effect, mitigates Section 3(k) exclusions under the Indian Patent Act, and ensures compliance with patentability standards in India and other jurisdictions.

[0081] The best mode known to the applicant involves a diagnostic module using a RISC-V-based NPU with 256 KB on-chip memory, an SX1262 LoRa transceiver, vibration and thermal sensors, and an internal coin-cell for 48-hour fallback operation. The preferred anomaly detection model is an LSTM-based classifier optimized for sub-2mA inference cycle

[0082] The invention offers distinct technical advantages not available in conventional diagnostic systems. First, it enables real-time anomaly recognition and autonomous local mitigation even when disconnected from central servers. Second, it supports reliable inter-device communication without relying on a fixed gateway or Internet connection. Third, it reduces equipment downtime, prevents silent failures, and extends hardware lifespan in remote deployments. These capabilities are critical in regions with fragile connectivity, making this invention highly relevant in cold-chain logistics, agricultural pumps, standalone solar inverters, and decentralized healthcare monitors.

[0083] This invention can be effectively deployed in a wide range of industrial and public service contexts, such as off-grid power stations, urban mobility fleets, agricultural automation, cold-chain medical logistics, and decentralized infrastructure monitoring. The compact design, autonomous fault handling, and secure communication make it well-suited for environments where conventional systems fail due to power, bandwidth, or reliability constraints. Specific deployment scenarios include LoRa-enabled diagnostic nodes on mining conveyors, remote vaccine storage units in tribal health centers, smart meters on electricity poles, and electric scooter hubs—demonstrating the system’s core strengths in decentralized intelligence and resilience to connectivity loss.

[0084] The invention is architected with long-term evolvability in mind. The modular hardware design allows the substitution of newer LoRa chipsets, AI cores, or sensor modules without altering the system logic. As the 6G standard matures, provisions exist to port the communication layer to ultra-reliable low-latency links while retaining diagnostic protocols. AI model containers follow ONNX and TFLite standards, ensuring migration across future microcontrollers. Quantum-safe cryptographic primitives can be integrated into future revisions using post-quantum signature libraries. The peer-to-peer mesh topology can expand to include other LPWAN technologies such as Sigfox or NB-IoT with firmware-level abstraction. Such forward compatibility ensures the invention remains relevant and patent-enforceable even a decade from filing.

[0085] In another embodiment, the system may integrate a lightweight blockchain or distributed ledger mechanism to record diagnostic events in a tamper-proof manner. Each anomaly log, including timestamps, fault class, and device ID, may be hashed and appended to a distributed chain either locally or across mesh peers. This approach not only strengthens the audit trail for high-integrity deployments such as healthcare and infrastructure but also provides verifiable logs that enhance trust during warranty claims or compliance audits by regulators or certifying authorities.

[0086] Additionally, the invention may adopt a zero-trust security model across its mesh communication framework. Under this architecture, each device continuously verifies the identity and integrity of its peers before any data exchange. Session keys for encrypted communication are dynamically generated and rotated on a per-transaction basis, much like modern authenticator applications. By employing cryptographic handshakes and micro-certificates validated by embedded secure elements or TPM (Trusted Platform Module) hardware, the system ensures that only authenticated nodes participate in the data mesh, thus mitigating risks of spoofing, replay attacks, or unauthorized access.

, Claims:Claim 1. A diagnostic module for predictive maintenance and fault handling, the module comprising:

(a) a neural processing unit (NPU) configured to execute on-device machine learning models for anomaly detection;

(b) a diagnostic sensor interface configured to receive input signals from one or more sensors including at least one of voltage, current, thermal, vibration, or environmental sensors;

(c) a LoRa transceiver configured for ultra-low power wireless communication using LoRa protocol; and

(d) a control logic circuit configured to manage power states, trigger diagnostics, and transmit anomaly data based on threshold events,

wherein the module is configured to operate independently of cloud connectivity and initiate local or peer-based mitigation actions.

Claim 2. The system as claimed in claim 1, wherein a plurality of said diagnostic modules form a mesh-enabled communication network configured to propagate fault-related data through peer-to-peer LoRa-based links using multi-hop routing, thereby enabling decentralized anomaly sharing and collaborative diagnostics in absence of a central gateway.

Claim 3. A method for predictive maintenance using the diagnostic module as claimed in claim 1, the method comprising:

(a) collecting real-time sensor data from one or more connected sensors;

(b) analyzing the sensor data using an embedded machine learning model to detect anomalies;

(c) generating a diagnostic summary including device ID, timestamp, anomaly score, and priority flag; and

(d) transmitting the summary over LoRa communication to peer devices or a remote server,

wherein the method further includes initiating mitigation actions based on context-aware logic, including throttling, rebooting, or peer notification.

Claim 4. The system as claimed in claim 1, wherein the diagnostic module includes a dynamically adaptive AI model configured to personalize anomaly thresholds based on fused sensor data, historical device behavior, and environmental changes, thereby improving fault detection specificity and reducing false positives.

Claim 5. The system as claimed in claim 1, wherein the diagnostic module is configured as a retrofit unit for integration with legacy devices lacking digital interfaces, and wherein the module detects operational anomalies using indirect physical indicators such as temperature signatures, electrical resonance, or casing vibrations.

Claim 6. The system as claimed in claim 1, wherein the diagnostic module further comprises:

(a) an encryption engine configured to secure transmitted diagnostic data using symmetric or asymmetric cryptographic algorithms; and

(b) a tamper detection circuit configured to monitor physical breaches, power anomalies, or unauthorized access events,

wherein upon detecting tampering, the module disables diagnostics, logs the breach, and transmits a tamper alert to the mesh network or designated receiver.

Claim 7. The system as claimed in claim 1, wherein the control logic is further configured to trigger context-specific mitigation actions based on operational parameters such as environment type, device urgency, user profile, or energy availability, thereby enabling adaptive fault response behavior.

Claim 8. The system as claimed in claim 1, wherein the diagnostic module is further configured to receive over-the-air (OTA) updates comprising firmware patches and AI model weights, and wherein the updates are validated using cryptographic hash checks and deployed using a dual-buffer mechanism to ensure rollback protection.

Claim 9. The system as claimed in claim 1, wherein the AI engine is configured to switch between simplified and complex anomaly detection models based on available energy, such that in low-power states a lightweight model is executed, while in high-power availability conditions, a deep learning model is used for enhanced inference accuracy.

Claim 10. The system as claimed in claim 1, wherein the diagnostic module includes an internal anomaly simulation engine configured to replay stored fault signatures periodically for self-testing purposes, thereby validating mesh communication, fallback logic, and response mechanisms in offline or audit scenarios.