Predictive incident management device and system using cross-sensor temporal patterns and scalable rule processors

Abstract

A predictive incident management system, consisting of: a sensor input module configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical and cyber sources; a temporal correlation control unit operationally coupled to the sensor input module, wherein the temporal correlation control unit is configured to normalize received data into a uniform time series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context markers, and is further configured to compute sliding window-cross-sensor correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities; a scalable rule processor that is communicatively linked to the control unit for temporal correlation, wherein the rule engine includes an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules that reference primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; A historical repository that is communicatively connected to both the temporal correlation control unit and the rule engine. The repository is configured to store tagged event histories, correlation motif dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions; and An incident response interface is operationally connected to the rule engine. The incident response interface is configured to trigger automated workflows, including the generation of tickets for IT service management, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols. the system is configured to perform predictive analyses based on temporal correlations between sensors and to execute context-aware, rule-based incident management in real time.

Description

Technical field

[0001] The present invention belongs to the field of intelligent monitoring, fault detection, and predictive maintenance technologies. It relates in particular to a device and system for predictive fault management that integrates heterogeneous sensor data, extracts temporal correlation patterns, and executes adaptive rules via a scalable control processor to anticipate, classify, and mitigate faults in industrial plants, IT data centers, and enterprise infrastructures. The invention also relates to a device structure that houses modular components such as sensor input units, correlation processing units, embedded control processors, storage repositories, and fault management interfaces in a unified machine form factor and is suitable for use in edge, fog, or cloud environments. BACKGROUND OF THE INVENTION

[0002] The modern industrial and IT ecosystem has evolved into an environment where virtually every critical system is continuously equipped with sensors, telemetry data, or software agents. These components generate real-time measurements from the mechanical, thermal, electrical, and computational domains. While this wealth of data enables fault prediction, it also complicates the capture of temporal dependencies across multiple data streams. A single fault, such as a slowing fan, can propagate across thermal, electrical, and computational layers, leading to cascading failures. Existing systems often lack the integrated capability to capture subtle, cross-sensor temporal relationships and cannot combine prediction with automated, context-aware control execution.

[0003] Conventional incident detection solutions are currently fragmented. Event correlation systems based solely on temporal proximity cannot adapt to changing system conditions. Sensor fusion approaches detect environmental anomalies but lack intelligent incident response mechanisms. Rule-based systems capture expert logic but cannot model temporal causality. Machine learning detects latent anomalies but is neither explainable nor verifiable, and cannot be implemented with operational rules. Edge analytics reduces latency but limits fault detection to individual devices, resulting in a loss of systemic context. This technological gap highlights the lack of a holistic framework that combines cross-sensor temporal learning with scalable, explainable rule sets to enable automated incident response.The present invention overcomes these limitations.

[0004] The evolution of modern industrial, information technology, and enterprise infrastructures has led to an unprecedented proliferation of sensors, telemetry interfaces, and monitoring software, each generating enormous streams of operational data. From vibration signals in rotating machinery and thermocouple readings in manufacturing plants to network packet tracking and performance indicators from cloud microservices, the collective ecosystem has become a dense, multimodal information network reflecting the state of complex systems. While such environments promise greater transparency into system health, they also pose significant challenges for effective incident detection and predictive maintenance.Traditional monitoring tools, which previously relied on fixed thresholds or simple anomaly alerts, are no longer sufficient to capture the subtle, nonlinear relationships between heterogeneous data sources that often serve as early indicators of failure. For example, a small voltage fluctuation might precede a motor failure, while packet retries can signal memory bottlenecks. Without an integrated approach to detecting these temporal and cross-sensor relationships, however, organizations face either delayed fault detection or a flood of uncorrelated alerts that overwhelm operators.

[0005] One of the first approaches to managing this complexity was event correlation systems, which attempted to group related events based on their temporal proximity. A well-known example is the use of Complex Event Processing (CEP) engines, which cluster events occurring within a predefined time window, such as ten seconds, thereby suppressing duplicate or redundant alarms. This method proved helpful in reducing noise and presenting operators with a more consolidated picture of incidents. However, its biggest limitation lies in the rigidity of its rule base. The correlation logic of such systems is hard-coded during design, meaning that any adaptation to new sensor types, changing process dynamics, or evolving infrastructure requirements necessitates manual reprogramming.By treating all events as temporally equivalent, these systems also ignore critical domain-specific semantics such as differences in sensor sampling rates, hierarchical dependencies between devices, or contextual factors like whether devices are undergoing maintenance or in active production. While these systems represent an early step toward more intelligent monitoring, they are not scalable in dynamic environments such as Industry 4.0 production lines or cloud architectures with constantly evolving workloads.

[0006] Another research focus aimed at improving accuracy through the use of sensor fusion techniques, particularly in IoT-based security and environmental monitoring systems. For example, the fusion of temperature, humidity, and gas concentration data into multivariate models for anomaly detection, such as ventilation failures, has been demonstrated. These approaches generally achieved higher hit rates than univariate methods for anomaly detection because they considered multiple dimensions of the problem. However, their scope is limited. Most designs are tightly tailored to specific sensor types and fixed plant topologies and lack the necessary flexibility for deployment in diverse industrial or IT environments.Furthermore, while they offer detection capabilities, they typically lack a decision-making layer that integrates detected anomalies into escalation protocols or business-context-driven prioritization. When such systems trigger an alert, human operators still need to interpret the alert, determine its severity, and initiate corrective actions. This deficiency proves problematic for mission-critical operations, as strict service-level agreements or compliance requirements demand rapid, automated responses.

[0007] Another approach evolved from rule-based incident management frameworks, which are widely used in IT data centers. These systems allowed operators to encode domain knowledge in the form of if-then rules. For example, a rule might read: "IF the disk I / O latency exceeds 30 ms AND the CPU idle time is below 10%, THEN report a memory bottleneck." Such frameworks had the advantage of being intuitive and transparent, allowing engineers to capture their expertise directly within the monitoring system. However, their scalability is a significant drawback. As the number of hosts, sensors, and workload patterns increases, the knowledge base grows exponentially, making maintenance unsustainable. Furthermore, these systems often lack the ability to capture temporal causality.This means that sequential patterns—such as a surge in network retransmissions leading to memory saturation—are not detected. As a result, these systems either miss critical early warning signs or generate overlapping alerts that confuse operators. In large data centers where workloads and dependencies change rapidly, the accuracy and recall of such systems deteriorate significantly.

[0008] The rise of machine learning, particularly deep learning architectures such as autoencoders and recurrent neural networks, has given rise to new methods for detecting latent anomalies in high-frequency telemetry. These approaches have yielded promising results by identifying subtle patterns in massive datasets that would be difficult to detect using rule-based or threshold systems. For example, long-short-term memory (LSTM) models are especially effective at capturing long-term temporal dependencies and are therefore well-suited for predicting failures in rotating machinery or production lines. However, the strength of these models is also their weakness: they function as black boxes. Their internal workings are often opaque, leaving operators unable to understand why a particular prediction or anomaly score was generated.This lack of explainability makes them difficult to use in regulated industries where audits and compliance require clear justifications for decisions. Furthermore, because these systems operate in isolation from rule-based engines, their ability to translate anomaly detection into actionable steps is limited. While they can indicate that "something is wrong," they cannot independently trigger workflows such as load redistribution, plant shutdowns, or spare parts logistics planning.

[0009] With the advent of edge computing, another dimension of predictive analytics emerged: fault detection and maintenance prediction closer to the data source. Edge analytics frameworks deploy lightweight predictive models directly on gateways or controllers located at the same site as the machines. This approach reduces latency and avoids the bandwidth costs of transmitting all telemetry data to the cloud. In certain use cases, such as the real-time control of manufacturing processes, the immediacy of edge analytics is essential. However, edge models are typically device-centric and univariate, monitoring only the local plant to which the gateway is connected. This isolated perspective prevents the detection of systemic patterns that extend across multiple machines or zones.For example, torque anomalies occurring synchronously across a conveyor line can remain invisible if each machine is analyzed in isolation. Furthermore, edge analytics without a federated control or orchestration layer lacks the ability to coordinate responses across distributed assets. While latency is reduced, the lack of a global context impairs overall predictive capability.

[0010] Taken together, these existing solutions highlight the fragmentation of predictive incident management approaches. Event correlation engines provide readable rules, but lack adaptability and temporal depth. Sensor fusion models improve detection accuracy, but are limited by tight design assumptions and the absence of decision automation. Rule-based IT incident management systems offer transparency, but collapse under complexity and fail to recognize causal sequences of events. Machine learning architectures excel at pattern recognition, but struggle with explainability and operational alignment. Edge analytics reduces latency, but at the cost of the systemic awareness required for holistic incident prediction.None of these solutions offers an integrated framework that simultaneously learns temporal, cross-sensor signatures, embeds them in scalable, human-verifiable rules, and orchestrates automated, context-sensitive incident responses across distributed infrastructures.

[0011] The drawbacks of existing approaches are particularly evident in the balance between accuracy, explainability, scalability, and operational integration. Systems optimized for one dimension often compromise others. For example, highly accurate neural networks may lack interpretability, while transparent rule systems fail to adapt to dynamic changes. Similarly, edge systems optimize latency at the expense of systemic context. The absence of a unified solution leads to inefficiencies, including increased downtime, resource misallocation, operator fatigue, and ultimately, higher operational risks. The need for a system that combines the predictive power of temporal analytics, the clarity of rules, and the agility of distributed deployment is therefore clear.Such a system would address the shortcomings of the current state of the art by enabling companies to proactively anticipate incidents, transparently explain decisions, and coordinate automated responses across different infrastructures. Objectives of the invention

[0012] The main objective of the invention is to provide a device and system for predictive incident management that can process heterogeneous sensor data streams, extract temporal correlation patterns, and apply scalable rule logic for proactive incident prediction and mitigation. A further objective is to develop a device that supports real-time monitoring and predictive control in industrial, IT, and hybrid infrastructures. Another objective is to make predictions and decision paths explainable, reduce false alarms through context-sensitive temporal models, and provide distributed intelligence through edge and cloud integration. Furthermore, the invention is intended to ensure scalability and adaptability, enabling system operators to adjust rule sets, escalation paths, and automated incident workflows. Summary of the invention

[0013] The invention provides a predictive incident management system implemented as both a logical architecture and a physical device. The system comprises a sensor input module for acquiring heterogeneous telemetry data, a temporal correlation control unit for determining cross-sensor time dependencies, a scalable rule processor for executing predictive rules, a historical incident archive for storing identified event patterns, and an incident response interface for executing automated remediation workflows.

[0014] In its device implementation, the invention is realized as a modular machine with several subsystems: (i) a sensor data acquisition card connected via wired and wireless interfaces, (ii) a dedicated temporal pattern recognition processor implemented on FPGA or GPU cores, (iii) a rule processor capable of performing complex event processing in real time, (iv) a non-volatile memory unit for historical patterns and incident fingerprints, and (v) a communication interface module supporting standard industrial and IT protocols such as MQTT, OPC UA, and REST APIs. The device integrates predictive AI-driven models with customizable rules, thus ensuring both proactive detection and operational explainability.

[0015] The present invention pursues several interrelated objectives that together overcome the weaknesses of existing fault management technologies while enabling a comprehensive, scalable, and explainable predictive framework. A key objective of the invention is to provide an intelligent fault management system that predicts potential failures before they occur by analyzing the temporal dependencies of heterogeneous sensor data streams. In contrast to the prior art, which is based either on static rules or opaque machine learning models, the invention combines the strengths of both approaches to generate predictive insights that are transparent, verifiable, and contextual.Another important objective of the invention is the introduction of a cross-sensor time pattern engine that continuously learns correlations and lead-follow relationships between different data sources such as mechanical sensors, thermal probes, network traces, and software agents.

[0016] By capturing recurring motifs and event patterns that historically precede failures, the system provides early warnings with fewer false alarms.

[0017] A further objective of the invention is the integration of a scalable rule processor that dynamically adapts to evolving infrastructures and workloads while simultaneously allowing human operators to define, test, and refine the operational logic. The rule engine is designed not only to detect anomalies but also to translate predictions into actionable responses such as automated remediation, escalation, or resource allocation. This ensures that incident management evolves from a purely diagnostic function to a proactive orchestration layer aligned with business processes. Another objective is to reduce operator fatigue and inefficiency caused by a flood of alerts through context-dependent time windows and prioritization strategies that ensure only relevant, correlated incidents are reported.

[0018] The invention also aims for deployment flexibility by supporting hybrid topologies that include edge devices, fog nodes, and cloud infrastructures. This distributed intelligence enables localized, low-latency predictions at the edge, while global patterns and federated rules can be enforced enterprise-wide for a holistic view. Another objective of the invention is the integration of explainable artificial intelligence components, so that every predictive outcome and triggered rule can be visualized, reviewed, and justified in accordance with legal requirements. By linking predictive analytics with traceable, rule-based decisions, the system enables both operations staff and compliance auditors to trust and validate the system's actions.

[0019] A key objective of the invention is to create a continuously improving predictive ecosystem that integrates historical incident data, operator feedback, and newly discovered correlation motifs into both pattern recognition and rule engines. This ensures that the system evolves over time, becoming more accurate, robust, and operationally aligned as it processes additional telemetry and incident data. By achieving these objectives, the invention establishes itself as a proactive, adaptive, and explainable framework for predictive incident management in industrial, IT, and enterprise environments. BRIEF DESCRIPTION OF THE FIGURE

[0020] These and other features, aspects, and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols consistently represent the same parts. The following applies: Fig. Figure 1 shows a block diagram of a device and system for predictive incident management using cross-sensor temporal patterns and scalable rule processors.

[0021] Experts will also recognize that the elements in the drawing are shown for the sake of simplicity and are not necessarily to scale. For example, the flowcharts illustrate the process by highlighting the main steps to enhance understanding of the aspects of this disclosure. Furthermore, with regard to the design of the device, one or more components of the device may be represented in the drawing by conventional symbols, and the drawing may show only the specific details relevant to understanding the embodiments of this disclosure, so as not to clutter the drawing with details that are readily apparent to those skilled in the art after reading this description. Detailed description of the invention

[0022] For a better understanding of the inventive principles, reference is made below to the embodiment shown in the drawing, which is described in specific terminology. However, this does not limit the scope of the invention. Changes and further modifications of the illustrated system, as well as further applications of the inventive principles, are possible, as would normally occur to a person skilled in the art in the field of invention.

[0023] It is clear to the person skilled in the art that the preceding general description and the following detailed description are exemplary and explanatory of the invention and are not intended as a limitation of it.

[0024] References in this specification to “an aspect”, “another aspect”, or similar expressions mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the expressions “in one embodiment”, “in another embodiment”, and similar expressions in this specification may all refer to the same embodiment, but need not.

[0025] The terms "includes," "include," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method that includes a list of steps may not only contain those steps but may also include other steps not expressly listed or inherent in such process or method. Likewise, the statement "includes..." in the case of one or more devices, subsystems, elements, structures, or components does not, without further limitations, preclude the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by a person skilled in the art in the field of the invention. The system, methods, and examples provided here serve only for illustration and are not to be construed as a limitation.

[0027] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0028] In Fig.Figure 1 shows a block diagram of a predictive incident management device and system that uses cross-sensor temporal patterns and scalable rule processors. System 100 comprises: a sensor input module (102) configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical, and cyber sources via standardized protocols such as OPC UA, Modbus TCP, SNMP, and gRPC; and a temporal correlation control unit (104) operationally coupled to the sensor input module. The temporal correlation control unit is configured to normalize received data into a uniform time-series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context tags. It is also configured to compute cross-sensor sliding-window correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities.a scalable rule processor (106) communicatively connected to the temporal correlation control unit, wherein the rule engine comprises an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules referencing primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; a historical repository (108) communicatively connected to both the temporal correlation control unit and the rule engine, wherein the repository is configured to store tagged incident histories, correlation motive dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions;and an incident response interface (110) that is operationally connected to the rule engine, wherein the incident response interface is configured to trigger automated workflows, including the generation of IT service management tickets, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols, wherein the system is configured to perform predictive analytics based on temporal correlations between sensors and to execute context-aware, rule-driven incident management in real time.

[0029] In one embodiment, the temporal correlation control unit (104) is configured to continuously generate correlation matrices over configurable windows with durations of 30 seconds, 5 minutes, and 1 hour. It is also configured to extract recurring event motifs by applying statistical and computational techniques, including cross-covariance, Granger causality, and dynamic time warping, such that each detected motif is associated with a historical probability of a fault occurring. Furthermore, the temporal correlation control unit publishes an early warning vector to the rule engine when the similarity of a live sensor sequence to a stored motif exceeds a learned threshold.

[0030] In one embodiment, the rule engine (108) is also configured to support parameterized rule templates, rule version control, and A / B testing of alternative rule sets. Rule conflicts are resolved based on the salience weights assigned to each rule, so that multiple simultaneously triggered rules are prioritized according to their operational severity. Furthermore, the rule engine adds contextual metadata to each reported incident, including contributing sensor identifiers, correlation values, and rule origin, to enable targeted remediation.

[0031] In one embodiment, the historical repository (108) is implemented as a non-volatile data store configured to permanently store tagged incident histories, supervised and unsupervised model parameters, and correlation motive dictionaries, and is also configured to link each recorded incident with its corresponding rule fingerprint and contributing sensor data, enabling post-mortem analyses and compliance audits to evaluate precision recall performance and suggest refinements to rules or motives.

[0032] In one embodiment, the incident response interface (110) is configured to integrate with both IT and operational technology ecosystems by simultaneously exposing REST APIs, MQTT endpoints, and industrial control connectors, and wherein the incident response interface is also configured to orchestrate automated workflows, including: (i) ticket creation in IT service management platforms such as ServiceNow or Jira, (ii) push notifications in collaboration platforms such as Slack and Microsoft Teams with embedded telemetry dashboards, (iii) invocation of orchestration scripts in systems such as Kubernetes, Ansible, or AWS Systems Manager to scale or roll back resources, and (iv) direct sending of control commands to programmable logic controllers or building management systems to activate redundant machines, throttle loads, or perform safe shutdowns.

[0033] In one embodiment, predictive analytics is enhanced by a continuous learning feedback loop comprising: (i) a supervised prediction module trained on labeled incident data to calculate failure probabilities, (ii) an unsupervised anomaly detection ensemble configured to detect new deviations in sensor data distributions, (iii) a Bayesian proof combiner that calibrates the outputs of the supervised and unsupervised modules before they are submitted to the rule engine, and (iv) an active learning queue that collects misclassified or missed incidents and requests subject matter experts to label them, so that approved labels are incorporated into the nightly retraining of correlation motifs and supervised models, thereby ensuring continuous improvement in predictive accuracy and rule relevance.

[0034] In one embodiment, the system can be deployed in hybrid environments comprising edge devices, fog nodes, and cloud infrastructure. The sensor input module at the edge performs preliminary normalization and buffering of the telemetry, the temporal correlation control unit at the fog nodes performs near real-time motif matching for localized predictions, and the cloud-based rule engine executes federated rule orchestration across multiple facilities, enabling both low-latency local responses and enterprise-level incident coordination.

[0035] In one embodiment, explainability is ensured by providing a visualization layer that displays rule execution paths, correlation motives, and decision contexts in graphical dashboards, and where the visualization layer queries the historical repository to reconstruct the origin of each triggered incident, thus meeting the auditability requirements according to compliance frameworks such as ISO 27001 and IEC 62443.

[0036] The predictive fault management system described in the preceding claims is based on a tightly integrated set of technical and architectural layers that together enable proactive fault detection, traceable decision-making, and automated fault correction. At the core of the system is a multi-stage technical pipeline that begins with the acquisition of heterogeneous sensor telemetry, proceeds through temporal correlation and motif recognition, and culminates in the execution of rule-based predictive logic with continuous learning enhancement. The following detailed description explains the claimed functional and technical components and forms the technical basis of the invention.

[0037] The system begins operation in the sensor input module, which is responsible for receiving telemetry data from various sources, including mechanical sensors such as accelerometers and vibration transducers, thermal sensors such as thermocouples and resistance temperature sensors, electrical sensors for detecting voltage and current fluctuations, and cyber or digital telemetry data from CPU counters, memory usage metrics, and packet-level traces. Each incoming data stream is normalized into a uniform time-series envelope, identified by fields such as sensor ID, microsecond-precise timestamp, metric name, value, and optional contextual tags such as device location, machine condition, or maintenance status.A buffer mechanism within the incoming pipeline ensures that late-arriving packets are held in short-term storage and resynchronized with global system clocks using NTP or PTP correction, thus enabling strictly ordered data streams for subsequent processing.

[0038] The normalized data are then transferred to the temporal correlation control unit, where the core predictive techniques come into play. The engine continuously generates moving correlation matrices over configurable time windows, typically spanning 30 seconds, 5 minutes, and one hour. These correlation matrices are not limited to pairwise statistics; rather, they calculate both low- and high-level dependencies across streams. Low-level dependencies include cross-covariance values ​​and Pearson correlation coefficients, while higher-level dependencies utilize more advanced techniques such as Granger causality analysis to detect lead-lag relationships and dynamic time warping to align asynchronous or nonlinear time-series sequences.Beyond statistical measurements, the temporal correlation control unit is capable of recognizing motifs, defined as recurring microsequences of multisensor events that have preceded past incidents. For example, an observed motif might link a four percent voltage drop in a power distribution module with a subsequent seven-degree Celsius temperature increase in a motor driver within a three-minute lead-lag interval.

[0039] The motif extraction technique is based on the continuous monitoring of live telemetry data against a dictionary of stored motifs built from historical event data. Each live sequence is compared to motifs using similarity measures derived from Euclidean distances, DTW orientation values, or motif embedding vectors learned from temporal convolutional networks. If a live sequence falls within a predefined similarity threshold of a known motif, the engine generates an early warning vector. This vector is enriched with metadata such as motif identifier, contributing sensor IDs, and calculated confidence levels, and then passed to the rule engine for subsequent evaluation. To prevent model drift and obsolescence, motif dictionaries are retrained nightly using weighted reservoir samples.This ensures that the system captures the evolving operational dynamics without over-adapting to current noise.

[0040] The rule engine is the core of the decision-making system. Architecturally, it is implemented as an in-memory runtime environment for complex event processing, enabling the evaluation of temporal rules with latencies of less than one second. The rules are expressed in a declarative, domain-specific language that even operations staff without in-depth knowledge of machine learning techniques can create. Rules can reference raw sensor readings, derived features such as moving averages or deltas, or early warning vectors generated by the correlation engine. For example, a rule might state: "IF the RMS amplitude of the vibration exceeds 3.5 g for at least 90 seconds AND a temperature delta of more than five degrees Celsius is detected on the same machine within 300 seconds, THEN a 'Bearing Wear' type incident with a 'High' severity level is triggered.""The rule engine supports parameterized templates, allowing generic logic to be applied to different systems, as well as version control of the rules to enable the safe implementation of refinements. Conflict resolution between simultaneously triggered rules is handled via salience weights, ensuring that the most operationally critical rule is executed first."

[0041] When a rule is triggered, the engine adds extensive contextual information to the incident object. This includes the specific sensors that contributed to the event, their correlation values, the matching motive identifier, and the origin of the rule that triggered the incident. The enriched incident object is then passed to the incident response interface, where automated remediation workflows are executed. These workflows can include creating tickets in IT service management systems such as ServiceNow or Jira, generating real-time notifications in platforms like Slack or Microsoft Teams with embedded visualizations, invoking orchestration frameworks such as Kubernetes or Ansible for scaling or rollback actions, and directly sending commands to programmable logic controllers (PLCs) or building management systems for physical load rebalancing, device shutdown, or failover activation.

[0042] The historical repository supports the transparency and continuous learning process of the invention. Every incident, motive, and rule execution is permanently stored in a non-volatile data repository. The repository maintains links between telemetry segments, motive identifiers, and triggered rules, thus enabling post-mortem analyses and compliance audits. For example, if a compliance auditor requests a justification for a shutdown event, the system can reconstruct the rule execution path and the correlation motives that led to the decision. This ensures traceability in accordance with regulatory standards such as ISO 27001 for IT systems and IEC 62443 for industrial control systems.

[0043] To achieve continuous improvement, the system uses a dual-track learning cycle. The supervised cycle trains predictive models, such as gradient-enhanced trees or temporal convolutional networks, using the tagged incident corpus and calculates failure probabilities for new telemetry segments. The unsupervised cycle runs ensembles of autoencoders or cluster models that tag novel anomalies not yet captured by existing rules or motifs. Both cycles feed their results into a Bayesian evidence combiner, which compares supervised probabilities and unsupervised novelty values ​​at calibrated confidence levels before they reach the rule engine. Any misclassified or missed incidents are placed in an active learning queue. Subject matter experts are then prompted to tag these cases.Once approved, the tags are reintroduced into the nightly training pipeline. This closed cycle ensures that the system's prediction accuracy, motif coverage, and rule relevance continuously improve without requiring time-consuming manual recoding.

[0044] The system is supported in hybrid environments. In edge deployments, the sensor input module and basic motif recognition run locally, delivering immediate, low-latency predictions for critical devices. In fog nodes, correlation analysis can aggregate telemetry data from multiple local devices to identify zone-wide patterns. In cloud deployments, full rule orchestration and the enterprise-scale historical repository operate, enabling federated coordination across multiple plants or data centers. This multi-layered deployment ensures that local anomalies are detected in real time, while holistically managing systemic patterns and enterprise workflows.

[0045] The system's explainability is further enhanced by a visualization layer that queries the repository and displays decision paths in graphical dashboards. Operators can view correlation heatmaps, motive alignments, rule execution sequences, and associated remediation workflows. This transparency ensures both operational confidence and compliance. The core technologies of this system harmonize statistical correlation, motive recognition, rule-based logic, supervised and unsupervised learning, and active human-in-the-loop validation, creating a predictive incident management framework that is both technically robust and operationally explainable. Device architecture

[0046] The invention is physically implemented as a rack- or enclosure-based device for predictive fault management. The enclosure is made of an aluminum alloy for thermal stability and electromagnetic shielding. The chassis is divided into five logical slots: 1. Sensor Input Bay: This bay contains analog-to-digital converters, protocol-specific network cards, and wireless transceivers for receiving telemetry data. It supports multiple input standards, including vibration sensors, thermocouples, current transformers, pressure sensors, CPU utilization feeds, and packet-level traces. 2. Temporal Correlation Processing Field: This field houses FPGA / GPU cards optimized for high-throughput time series analysis. It performs temporal correlation techniques such as dynamic time distortion, Granger causality, and motif extraction, and works with moving correlation matrices that continuously align and compare data across multiple timescales. 3. Rule Engine Area: The rule processing subsystem consists of multi-core processors optimized for in-memory event processing. The rule engine supports declarative domain-specific languages ​​for representing temporal event dependencies, rule prioritization, and context-dependent escalation protocols. 4. Historical Archive: A non-volatile solid-state storage device is integrated to store labeled event histories, motive dictionaries, rule versions, and decision logs. This ensures explainability and verifiability. 5. Incident Response and Communication Bay: This bay provides physical and logical interfaces for integration into enterprise workflows. It includes Ethernet, serial, and wireless interfaces, as well as pre-installed connectors for ITSM tools (ServiceNow, Jira), industrial controllers (PLCs, SCADA systems), and orchestration tools (Kubernetes, Ansible).

[0047] The present invention relates to the fields of intelligent monitoring, predictive maintenance, and automated fault management. More specifically, it is a framework for predictive fault management that integrates heterogeneous sensor telemetry, temporal correlation analysis, and scalable rule-based engines to predict and minimize faults in industrial, enterprise, and IT infrastructures. The invention lies at the intersection of multi-sensor data processing, temporal pattern recognition, complex event processing, and automated response orchestration, and is particularly applicable to Industry 4.0 manufacturing systems, cloud and data center operations, smart building environments, and other mission-critical infrastructures requiring proactive system integrity management.

[0048] The drawing and the preceding description show examples of embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another embodiment. For example, the sequence of the processes described here can be changed and is not limited to the manner described here. Furthermore, the actions of a flowchart need not be implemented in the sequence shown; nor does it necessarily have to be performed by all actions. Actions that are not dependent on other actions can also be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations are possible, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use. The range of embodiments is at least as broad as specified in the following claims.

[0049] Advantages, further benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and all components that can lead to an advantage, benefit, or solution occurring or becoming more apparent are not to be construed as critical, necessary, or essential features or components of individual or all claims. REFERENCES 100 A device and system for predicting incidents using temporal patterns from various sensors and scalable control processors. 102 Sensor input module 104 Temporal Correlation Control Unit 106 Scalable Control Processor 108 Historical Archive 110 Interface for Incident Response

Claims

[1] A predictive incident management system consisting of: a sensor input module configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical and cyber sources; a temporal correlation control unit operationally coupled to the sensor input module, wherein the temporal correlation control unit is configured to normalize received data into a uniform time series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context markers, and is further configured to compute sliding window-sensor cross-correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities; a scalable rule processor that is communicatively linked to the control unit for temporal correlation, wherein the rule engine includes an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules that reference primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; A historical repository that is communicatively connected to both the temporal correlation control unit and the rule engine. The repository is configured to store tagged event histories, correlation motif dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions; and An incident response interface is operationally connected to the rule engine. The incident response interface is configured to trigger automated workflows, including the generation of tickets for IT service management, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols. the system is configured to perform predictive analyses based on temporal correlations between sensors and to execute context-aware, rule-based incident management in real time. [2] System according to claim 1, wherein the temporal correlation control unit is configured to continuously generate correlation matrices over configurable windows with durations of 30 seconds, 5 minutes and 1 hour, and is further configured to extract recurring event motifs by applying statistical and computational techniques, including cross-covariance, Granger causality and dynamic time distortion, such that each detected motif is associated with a historical probability of the occurrence of a fault, and wherein the temporal correlation control unit publishes an early warning vector to the rule engine when the similarity of a live sensor sequence to a stored motif exceeds a learned threshold. [3] System according to claim 1, wherein the rule engine is further configured to support parameterized rule templates, version control for rules and A / B testing of alternative rule sets, and wherein rule conflicts are resolved based on the salience weights assigned to each rule, such that multiple simultaneously triggered rules are prioritized according to operational severity, and wherein the rule engine adds contextual metadata to each reported incident, including contributing sensor identifiers, correlation values ​​and rule origin, to enable targeted remediation. [4] System according to claim 1, wherein the historical repository is implemented as a non-volatile data storage device configured to permanently store tagged incident trajectories, supervised and unsupervised model parameters and correlation motif dictionaries, and furthermore configured to link each recorded incident with its corresponding rule fingerprint and contributing sensor data. [5] System according to claim 1, wherein the system is deployable in hybrid environments comprising edge devices, fog nodes and cloud infrastructure, and wherein the sensor input module at the edge performs preliminary normalization and buffering of the telemetry, the temporal correlation control unit at the fog nodes performs motif matching in near real time for localized predictions, and the rule engine provided in the cloud performs federated rule orchestration across multiple facilities, thereby achieving both low-latency local responses and enterprise-scale incident coordination. [6] System according to claim 1, wherein explainability is ensured by providing a visualization layer that displays rule execution paths, correlation motives and decision contexts in graphical dashboards, and wherein the visualization layer queries the historical repository to reconstruct the origin of each triggered incident.

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