Chemical plant failure database system based on quality management cycle

Abstract

The present application relates to the technical field of chemical equipment failure data management, and discloses a chemical equipment failure database system based on quality management cycle, which constructs a standardized classification coding system of three orthogonal dimensions of functional safety, equipment integrity and loop condition, and associates four types of data of equipment main data, failure events, inspection data and audit traceability as full life cycle digital twin archives by taking equipment position number as a mandatory foreign key, and sets CMMS interface NLP assisted classification, equipment template inheritance, SIL level compliance automatic verification and field level audit traceability mechanism, so that the system can eliminate the cross-enterprise / cross-equipment data terminology barriers, and finally realize the beneficial effects of unified structured storage of multi-source heterogeneous failure data and full-link traceability.

Description

Technical Field

[0001] This invention relates to the field of chemical equipment failure data management technology, specifically a chemical equipment failure database system based on a quality management cycle. Background Technology

[0002] Safety Instrumented Systems (SIS) and various general chemical process equipment are the core safeguards for safe production in chemical processes. Safety Instrumented Function (SIF) testing and equipment operation and maintenance data are the core basis for conducting Safety Integrity Level (SIL) verification, failure probability assessment, and safety production risk management. Accurate, comprehensive, and standardized equipment failure data are the key foundation for ensuring the functional safety of chemical equipment, avoiding production safety accidents, and optimizing equipment operation and maintenance strategies. However, the current system for collecting, managing, and applying chemical equipment failure data in the chemical industry is still imperfect, and the industry generally faces many technical pain points, severely restricting the accuracy and standardization of equipment safety verification and risk assessment.

[0003] Secondly, there is a technical limitation due to the incomparability of data. The operating conditions, environmental conditions, equipment stress states, and daily maintenance practices of equipment in the chemical process industry differ significantly from those in nuclear power, offshore oil, and other industries. The causes and characteristics of equipment failure vary across these industries. Currently, the industry commonly uses failure rate data directly across sectors to calculate the Probability of Hazardous Failure (PFDavg). However, the poor compatibility between operating conditions and data leads to significant deviations in the final calculation results, failing to accurately reflect the actual failure state of chemical equipment and greatly reducing the accuracy and reliability of SIL verification results.

[0004] Finally, there is a break in the data application chain. In the existing technical system, the actual inspection and testing results of equipment are only used for single equipment inspection and verification, without forming a closed-loop feedback mechanism, and cannot be effectively fed back into the safety integrity level verification calculation process. This results in technicians being unable to verify the consistency between the actual failure probability of the equipment and the initial design assumptions in real time, making it difficult to dynamically assess the safety integrity level of the equipment, and unable to achieve dynamic verification and optimization of equipment safety performance. The functional safety management of equipment lacks closed-loop, dynamic, and scientific rigor. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a chemical equipment failure database system based on a quality management cycle. It features standardized access to multi-source data, traceability of failure events and equipment master data, automatic calculation of failure rate and dual-dimensional verification of SIL (Solution Indicator Lack) and continuous optimization based on PDCA (Plan-Do-Check-Act) closed-loop feedback. This solves the problems of inconsistent data standards, static storage failing to support dynamic evaluation, disconnect between SIL verification and field operations, and lack of a closed-loop improvement mechanism in existing systems.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a chemical equipment failure database system based on a quality management cycle. The system comprises a five-layer technical architecture, seven functional modules, and six expandable modules distributed according to the architecture layers, as detailed below: The five-layer technical architecture consists of a first-layer data access layer, a second-layer classification standard layer, a third-layer data management layer, a fourth-layer computing engine layer, and a fifth-layer application layer. The first-layer data access layer is responsible for acquiring external multi-source heterogeneous raw data through four channels: Proof Test import, CMMS interface, inspection data import, and manual entry. The second-layer classification standard layer is responsible for establishing a unified coding system for equipment classification, failure modes, and PERD loop conditions, providing a standardized semantic foundation for the upper layers. The third-layer data management layer is responsible for the structured storage, multi-dimensional correlation, and audit traceability management of equipment master data, failure events, and inspection data. The fourth-layer computing engine layer is responsible for performing failure rate parameter statistics, chi-square confidence assessment, and PFDavg / PFH reliability index calculation. The fifth-layer application layer is responsible for providing end users with decision support applications such as SIL verification calculation, visualization dashboards, and professional report output. The seven functional modules consist of a Failure Mode Classification (FMCC) standard library, a failure data acquisition module, an equipment master data management module, an automatic reliability parameter calculation module, a SIL verification calculator, and a statistical analysis and visualization dashboard and report output module. The FMCC standard library is the underlying core, and its output PDS failure categories and failure mechanism codes are referenced by the data acquisition and equipment master data modules. The failure data acquisition module, based on the coding rules of the classification standard library, transforms external raw records into standardized failure events through four channels and stores them in the management layer. The equipment master data management module, also based on the classification standard library, registers three-level classifications for each device and accumulates operating hours, providing denominator data for the calculation layer. The automatic reliability parameter calculation module calculates and outputs the failure event counts and operating hours provided by the previous two modules. , Failure rate parameters for SFF and DC; the SIL verification calculator directly retrieves the output from the reliability calculation module. The parameters, combined with the MooN structure, HFT and A / B type classification in the equipment master data, complete the dual-dimensional judgment of failure quantity verification and structural constraint verification; the statistical dashboard and report output are parallel to all calculation results and feed the actual verification results back to the design assumptions, forming a complete closed loop from field testing to SIL verification and then to optimization suggestions; The six expandable modules consist of a mobile data acquisition app, a multi-organizational data federation module, a LOPA integration module, an MOC linkage module, a digital twin interface, and a device reliability digital twin module. The mobile data acquisition app is deployed at the first data access layer, allowing on-site inspection personnel to input failure events via mobile phone photos and voice recording, which are then cached offline and synchronized to the system. The multi-organizational data federation module is deployed at the third data management layer, supporting multiple enterprises to share failure rate statistics without exposing original records through authorized / anonymized calculations, gradually establishing industry benchmarks. The LOPA integration module is deployed at the fifth application layer, backfilling SIL verification results to the Layer of Protection Analysis worksheet with one click and automatically updating the PFD values ​​of independent protection layers. The MOC linkage module is deployed at the third data management layer, interfacing with the change management system to automatically trigger failure risk assessment updates when equipment / processes change. The digital twin interface is deployed at the fourth computing engine layer, accessing DCS / PLC data streams in real time and dynamically updating failure probability estimates. The device reliability digital twin module is deployed at the fifth application layer, fitting the equipment's full lifecycle failure rate curve based on the Weibull distribution to predict future failure trends.

[0007] Preferably, the first data access layer provides four external data acquisition channels; (1) Channel 1: Import the verification test report template automatically generated by the system based on SRS through the Proof Testing template. It also supports batch import of Excel, PDF OCR recognition and online submission of Web forms. After import, the device master data is automatically associated with the device tag number and updated. / statistics; (2) Channel 2: Supports three methods through the CMMS interface: direct connection to REST API, batch import of CSV / Excel files and timed synchronization of intermediate database. It is used to extract fields including device ID, failure time, failure description, work order number and maintenance duration. The failure category is manually reviewed and confirmed as DU / DD / SU / SD. (3) Channel 3: Supports wall thickness measurement, NDT non-destructive testing report PDF import, corrosion monitoring data import and online monitoring data import through inspection data import, and automatically performs RBI related calculations after import; (4) Channel 4: Manual input includes quick input forms, standard input forms and batch Excel import, and plans to expand mobile voice input; each failure record will uniformly include the record ID, device ID, failure time, data source, test program number, pass / fail result, PERD loop status classification, PDS failure category, failure mechanism, failure root cause, failure impact, work order number, MOC number, operation mode context, input person and review status standard fields after entering the database.

[0008] Preferably, the second classification standard layer consists of a classification system with three orthogonal dimensions, as follows: (1) Classification by Failure Consequences - PDS Failure Categories: Failures are classified into four categories based on two orthogonal dimensions: undetected hazardous failures, detected hazardous failures, undetected safe failures, and detected safe failures; the failure rates of the four categories satisfy an additive relationship: = + + + The aggregated parameters include the total safety failure rate. = + Total risk failure rate = + Diagnostic coverage DC = / ( + Safety Failure Score (SFF) = ( + ) / =1- / ; (2) Classification by failure mechanism: It is divided into five categories: pressure boundary failure, functional failure, instrument control failure, pressure reducing device failure and damage mechanism; (3) Classified by PERD circuit status: divided into interlocking function failure, alarm function failure, abnormal process variable indication and false discrete indication.

[0009] Preferably, the third data management layer is used to store and manage device master data, failure event data, inspection data, and audit traceability information. This layer consists of the following sub-modules: (1) Equipment master data management registers equipment according to a three-level equipment classification system: L1 functional category → L2 equipment group code → L3 technical type code; equipment master data management pre-builds equipment templates based on the three-level classification system, and new equipment can inherit the default parameters of the L3 level when registering, and is divided into the following six sub-modules according to functional domain: Identification and Traceability Information Submodule A: Includes device tag number, manufacturer, model, serial number, firmware version, device barcode / QR code, and purchase order number; Sub-module B, Hierarchical Attribution and Spatial Location: Includes factory code, unit code, installation location, floor / elevation, GPS coordinates, and P&ID drawing number; Installation and Service Information Submodule C: Includes commissioning date, installation date, expected decommissioning date, cumulative operating hours, operating mode, and average number of start-ups and shutdowns per year; Submodule C supports dual calculation modes for cumulative operating hours: the system automatically calculates from the commissioning date according to calendar time, or calculates based on the actual operating / downtime in the CMMS work order using a weighted cumulative method. Users can select the calculation rules when registering the equipment. Service Conditions Submodule D: Includes medium name, medium phase, medium corrosion level, design temperature, operating temperature, design pressure, operating pressure, service type, and environmental conditions; Security Attributes Submodule E: Includes SCE identifier, target SIL level, associated SIF number, SIF loop role, MooN voting structure, A / B type classification, hardware fault margin (HFT), and security failure fraction (SFF).

[0010] The Changes and Documents Association submodule F includes the MOC association number, MOC date, FMEDA report reference, SRS reference, SIF verification report reference, equipment photos, and notes. (2) Failure data storage: Store all failure event records from the four acquisition channels of the failure data acquisition module. The device ID is used as a mandatory foreign key to establish a relationship with the device master data. When each failure record is entered into the database, the system automatically verifies the validity of the device ID and updates the cumulative number of failures and the last failure time of the corresponding device in the device master data in a cascading manner. (3) Inspection data storage: Store wall thickness measurement, NDT non-destructive testing, corrosion monitoring and online monitoring data; The wall thickness reduction and corrosion rate calculation results in the inspection data storage will be automatically fed back to the equipment master data submodule D, triggering dynamic correction of the expected retirement date and reassessment of the remaining service life RL, realizing real-time linkage between inspection findings and equipment service records; (4) Audit traceability: Records the creation, modification and review history of all data, and supports full-link traceability; Audit traceability not only records the operator, operation time and operation type, but also records the comparison of field-level values ​​before and after.

[0011] Preferably, the workflow of the fourth computing engine layer is as follows: S1.1 Data Filtering and Preprocessing: Filter out failure records whose audit status has been confirmed, group them by equipment type, and exclude data with atypical operating conditions; S1.2 Failure Count Statistics: Counted by PDS Category , , , The failure count is classified and counted according to failure mechanism and PERD loop condition, and the failure count matrix of each equipment type is output. S1.3 Failure rate parameter calculation: The core parameter calculation formulas are as follows: (1) Failure rate due to undetected hazards: = ,in, The cumulative number of undetected hazardous failures; EDH represents the cumulative number of equipment operating hours. (2) Hazard detection failure rate: = ,in, This represents the cumulative number of times a hazard detection failure has occurred. (3) Safety failure rate not detected: = ,in, The cumulative number of undetected safety failures; (4) Safety detection failure rate: = ,in, This represents the cumulative number of times a safety inspection has failed. (5) Overall safety failure rate: = + ,in, Failure rate was not detected for safety reasons; (6) Overall risk failure rate: = + ,in, For the failure rate of hazard detection, Failure rate due to undetected danger; (7) Overall failure rate: = + ; (8) Safety Failure Fraction: SFF = ,in, For the overall safety failure rate, For the failure rate of hazard detection, Total failure rate; (9) Mean time between failures: ,in, Total risk failure rate; S1.4 Confidence Assessment: According to GB / T 21109.1, chi-square distribution estimation is used. Upper limit of confidence interval: = ,in Confidence level CL, degrees of freedom Chi-square quantiles, The number of DU failures observed; key Quantile reference: (When r=0); Sample size determination rules: ≥5 indicates acceptable statistical certainty. =1~4 indicates insufficient statistical certainty When =0, it cannot be calculated directly. Simultaneously, the system automatically compares the statistical results with the CCPS LOPA / PDS reference benchmark. S1.5 Result Output and Verification: Output the calculated reliability parameters , , , , , , The upper limit of the 70% confidence interval is output to the device master data security attribute submodule, and then output to the SIL verification calculator to be compared with the design assumption values ​​and generate a device reliability report. Simultaneously, this layer automatically performs RBI-related calculations based on the test data: (1) Residual corrosion allowance : ,in, This is the current measured wall thickness. Minimum allowable wall thickness; (2) Corrosion rate CR: ,in, This is the measured wall thickness from the previous test. This is the currently measured wall thickness. The time interval between two tests; the unit of CR is mm / year; (3) Remaining life RL, where RCA is the remaining corrosion allowance and CR is the corrosion rate; the unit of RL is years; (4) Maximum test interval (MII): Where RL is the remaining lifetime and IF is the interval factor.

[0012] Preferably, the fifth application layer will calculate the reliability parameters output by the engine layer. , , , , , , The 70% confidence interval upper limit is used for SIL validation calculation, statistical analysis and visualization, and report output. This layer consists of a SIL validation calculator, a statistical analysis and visualization dashboard, and a report output module. Specific functions are as follows: (1) The SIL verification calculator directly uses / retrieves the output of the fourth layer. The parameters are used to perform dual-dimensional SIL verification. The final SIL is the smaller value between the failure quantity SIL and the structural constraint SIL. The PFDavg / PFH value, SIL level determination and TI optimization suggestions are output simultaneously. (2) Statistical analysis and visualization dashboards are based on the results of previous calculations and present four dashboards in a graphical manner: failure trend analysis, enterprise benchmarking, MI performance indicators and comprehensive dashboard. (3) The report output module generates five types of reports based on the previous calculation results: SIL verification report, equipment reliability report, MI performance report, TI optimization report and audit traceability report.

[0013] Preferably, the SIL verification calculator directly uses / retrieves the output of the fourth layer. Parameters, perform two-dimensional SIL verification: (1) Dimension 1: Failure quantity verification is based on the MooN architecture, TI, MRT, MTTR, β factor and PTC parameters. The failure quantity value is calculated according to the PFDavg / PFH formula of the corresponding architecture, and the failure quantity SIL is determined by referring to the SIL level table. (2) Dimension 2: Structural constraint verification is based on SFF, HFT and A / B type classification, and the structural constraint SIL is determined by referring to the Route 1H decision table.

[0014] Preferably, the statistical analysis and visualization dashboard presents four dashboards—failure trend analysis, enterprise benchmarking, MI performance indicators, and a comprehensive dashboard—using graphical means based on the preceding calculation results. (1) Kanban 1: Failure Trend Analysis Kanban includes a failure rate time series line chart, a pie chart composed of DU / DD / SU / SD, a bar chart of failure mechanism distribution, and a comparison chart grouped by process medium; (2) Benchmarking Board Two: Enterprise Benchmarking Board including Failure Rate Benchmarking Radar Chart, Compare bar charts, anonymous data contribution statistics cards, industry benchmark trend line charts, and SIL achievement rate benchmarking dashboards; among them, the anonymous data contribution mechanism allows enterprises to upload only anonymized statistical data, and calculate industry benchmarks through federated statistical methods without exposing the original failure records; (3) Dashboard 3: The MI performance indicator dashboard includes a leading indicator progress bar / dashboard, a lagging indicator trend line chart, a deterioration trend scatter plot + trend line, and a SIF health status card with red, yellow and green lights. (4) Kanban 4: The integrated cockpit includes a digital card summarizing the total number of equipment, a pie chart summarizing the SIL status, an alarm list, and a summary of the statistical period.

[0015] Preferably, the report output module generates five types of reports: SIL verification report, equipment reliability report, MI performance report, TI optimization report, and audit traceability report, as follows: (1) Report 1: SIL verification report includes basic SIF information, equipment list, failure quantity verification, structural constraint verification, final SIL determination, actual vs design comparison and TI optimization suggestions, conclusions and recommendations; (2) Report 2: Equipment Reliability Report includes equipment group overview, failure rate statistics, aggregated parameters, confidence analysis, industry benchmark comparison, trend analysis and LOPA data suggestions; (3) Report 3: MI performance report includes ITPM ​​completion rate, failure trend, SIF failure statistics and PSM / AIM compliance; (4) Report 4: TI Optimization Report includes SIF overview, optimization analysis, risk analysis, constraint check and priority ranking; (5) Report 5: Audit traceability report includes the entire chain of failure events, change history, SIL verification history and data integrity.

[0016] Preferably, the seven functional modules in the system are located as follows: The failure mode classification standard library is located in the second classification standard layer. It is a lower-level dependency that provides standardized classification code references for the fields of data acquisition by the failure data acquisition module and equipment master data management module. The failure data acquisition module is located in the first data access layer. Based on the failure mode classification standard library, it collects external data through four channels. After collection, the data is entered into the system database. Its failure records provide raw data for the reliability parameter automatic calculation module. The device master data management module is located in the third data management layer. Based on the failure mode classification standard library, it manages device master data and provides the device runtime and device attribute parameters to the reliability parameter automatic calculation module. The automatic reliability parameter calculation module is located in the fourth-layer calculation engine layer. Based on the failure records of the failure data acquisition module and the device runtime of the device master data management module, it executes a five-stage calculation process and outputs... , , , , , , and the upper limit of the 70% confidence interval; The SIL verification calculator is located in the fifth application layer and directly uses / retrieves the output of the reliability parameter automatic calculation module. Parameters are set to perform failure quantity verification and structural constraint verification, and the final SIL level and TI optimization suggestions are output. The statistical analysis and visualization dashboard is located in the fifth application layer, based on the automatic reliability parameter calculation module. The parameters and SIL verification calculator provide SIL verification results, offering a visual display of four dashboards: failure trend analysis, enterprise benchmarking, MI performance indicators, and a comprehensive dashboard. The report output module is located in the fifth application layer, based on the automatic reliability parameter calculation module. The parameters and SIL verification results from the SIL verification calculator are displayed as parallel outputs, generating five types of reports.

[0017] Compared with existing technologies, this invention provides a chemical equipment failure database system based on a quality management cycle, which has the following beneficial effects: 1. This invention constructs a standardized classification and coding system with three orthogonal dimensions: Functional Safety (PDS four categories), Equipment Integrity (failure mechanism), and Circuit Status (PERD). It uses the equipment tag number as a mandatory foreign key to link four types of data—equipment master data, failure events, inspection data, and audit traceability—into a full lifecycle digital twin archive. At the same time, it sets up CMMS interface NLP-assisted classification, equipment template inheritance, automatic verification of SIL rating compliance, and field-level audit traceability mechanism. This enables the system to eliminate cross-enterprise / cross-equipment data terminology barriers and ultimately achieve the beneficial effects of unified structured storage and full-link traceability of multi-source heterogeneous failure data.

[0018] 2. This invention employs a five-stage standardized calculation process built into the fourth-layer calculation engine: data filtering → itemized statistics → failure rate calculation → chi-square confidence assessment → bias verification. It uses a 70% chi-square confidence upper limit as... Conservative estimates are used to address statistical uncertainties in small samples, and an automatic alert mechanism is set up if the actual failure rate deviates from the design assumptions by more than 20%. Additionally, the SIL validation calculator directly retrieves field statistics. The parameters are used to perform dual-dimensional SIL verification (failure quantity verification + structural constraint verification), achieving the beneficial effect of fully automatic closed-loop feedback and quantitative deviation detection from field proof test data to SIL verification loop.

[0019] 3. This invention sets up a three-in-one modular collaborative architecture of SIL verification calculator (analysis engine) + visualization dashboard (dynamic monitoring) + report output (static archiving) through the fifth application layer. At the same time, it pre-installs digital twin interface (Bayesian dynamic update) and equipment reliability digital twin module (Weibull full life cycle fitting) in the extension layer, realizing a leapfrog upgrade from single static verification to real-time dynamic evolution, and from single functional safety verification to dual-track decision support of functional safety + asset integrity. Attached Figure Description

[0020] Figure 1This is a system framework diagram of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 This is a chemical equipment failure database system based on a quality management cycle. The system consists of a five-layer technical architecture (data access layer, classification standard layer, data management layer, computing engine layer, and application layer), seven core functional modules (failure mode classification standard library, failure data acquisition module, equipment master data management module, automatic reliability parameter calculation module, SIL verification calculator, statistical analysis and visualization dashboard and report output module), and six expandable modules distributed across the architecture layers (mobile data acquisition APP, multi-organization data federation module, LOPA integration module, MOC linkage module, digital twin interface and equipment reliability digital twin module). The backend is based on Python. FastAPI provides scientific computing services (including numpy / scipy / sympy), with a dual-interface front-end built on React / Electron, and databases using PostgreSQL (online version) and SQLite (offline version). Reports are generated by matplotlib and ReportLab. The system follows the PDCA quality management cycle, using SIF verification testing as the data acquisition entry point, standardized classification as the data organization core, and SIL verification calculation as the data application exit point. It simultaneously covers SIS equipment and general process equipment, aiming to solve industry pain points such as fragmented data sources, incomparable data, inconsistent terminology, and broken data → SIL verification links.

[0023] The advantages are: the six-module architecture design enables the system to evolve from a single factory and single user to a multi-organization federated ecosystem, and from static periodic statistics to dynamic real-time digital twins. The multi-organization federated module and the digital twin interface represent cutting-edge industry directions; the former uses federated learning to resolve the conflict between data sharing and trade secret protection, while the latter uses Bayesian updates to transform static data into digital twin data. The parameters are upgraded to live documents that evolve dynamically with the DCS data flow, providing a feasible technical path for the digital transformation of functional safety.

[0024] Table 1 of Full Names of English Abbreviations ; The six scalable modules mentioned above supplement and enhance the seven core functional modules. They are not essential components of the core processes, but rather extension solutions for specific scenarios, such as multi-factory deployment, data sharing alliances, LOPA system integration, MOC process integration, digital infrastructure construction, and on-demand deployment of advanced reliability analysis. The modules are distributed according to the architectural layers as follows: Mobile data acquisition apps are used in scenarios with multiple factory deployments, wide distribution of equipment areas, and unstable on-site Wi-Fi / 4G signals. They can effectively solve the problems of delay and error in the secondary transcription of paper records, and advance the collection time of failure events from "entering after returning to the office" to "the first scene when arriving at the equipment". The multi-organization data federation module is suitable for scenarios where multiple enterprises join a data sharing alliance. By expanding the data base (increasing the N value), it effectively reduces statistical uncertainty, gradually establishes a reliable benchmark database with industry credibility, and strictly protects the business secrets of all participating parties. The LOPA integration module is suitable for scenarios where enterprises use LOPA software and want to establish a seamless SIL verification loop. It enables LOPA and SIL verification to share a set of reliability parameters calibrated with actual statistical data, eliminating the bias in conclusions caused by inconsistent parameter sources in the two analysis methods for the same equipment. The MOC linkage module is suitable for scenarios where enterprises have established formal MOC processes and want to dynamically link change management with reliability data, ensuring that failure risk assessments after equipment / process changes can be triggered and executed in a timely manner, rather than relying on manual memory-driven processes. The digital twin interface is suitable for enterprises that already have a high level of digital infrastructure (well-developed DCS / PLC system and sound real-time data acquisition network) and want to achieve functional safety and live documentation, so that operators always have access to the latest failure probability estimates. The equipment reliability digital twin module is suitable for scenarios where advanced analysis can be enabled after sufficient data (N>30) has been accumulated during several years of system operation.

[0025] By integrating the entire functional safety SIL verification process with process equipment integrity management (RBI) into the same technical architecture, a PDCA closed loop from field failure data to SIL verification is established, enabling one data acquisition to output dual value (functional safety + asset integrity). The first data access layer provides four external data acquisition channels; (1) The verification test report template in Channel 1 includes the pre-filled device tag number, target SIL, test procedure number, last test date, and acceptance criteria; (2) In Channel 2, the REST API direct connection pushes data in JSON format, and the failure description is extracted using NLP keywords to suggest failure categories; (3) The wall thickness measurement in channel 3 is imported via UT thickness gauge .csv / .txt, which includes the measurement point location, actual wall thickness, nominal wall thickness, minimum allowable wall thickness and measurement date. The online monitoring data is read by OPC / Modbus interface; (4) The minimum required fields for the form in channel 4 are: device ID + time + fault description. The ID is automatically generated and its data source is enumeration, including: Proof Test / CMMS / inspection / manual. The PDS failure category includes DU / DD / SU / SD. The operation mode context includes normal production / start-up / stop / maintenance. The review status includes pending review / confirmed / rejected. External data is collected through this layer and then entered into the system database.

[0026] The advantages are: through a four-channel parallel design of "template import + CMMS interface + inspection data + manual entry", the system can achieve unified collection and structured conversion of multi-source heterogeneous data such as Proof Test, CMMS maintenance work orders, NDT / corrosion monitoring, etc.; among them, the CMMS interface introduces NLP keyword extraction to assist classification, and the inspection data channel realizes one-stop import of UT / NDT / corrosion monitoring data and automatically triggers RBI calculation, which fundamentally solves the long-standing pain points of "fragmented sources, unstructured formats, and inconsistent standards" of failure data in the chemical industry, and integrates the data fragments that were originally scattered in various business systems into calculable structured failure events.

[0027] The second classification standard layer provides a standardized classification coding system for all data and is the core of the software's data organization. This layer consists of a classification system with three orthogonal dimensions, as follows: (1) Classification by Failure Consequences - PDS Failure Categories (Functional Safety Perspective): Failures are classified into four categories based on two orthogonal dimensions: failure consequences (comparison between safety and danger) and diagnosability (comparison between detected and undetected). Dangerous Undetected Failure (DU): Dangerous failures that cannot be detected by online diagnostics and can only be detected by Proof Test; this is the core input for PFDavg calculation. ); b. Hazardous Detection Failure (DD): Hazardous failures that can be automatically diagnosed and detected, serving as input for SFF calculations. ); c. Undetected Safety Failure (SU): Safety failures that are not diagnosed or detected and lead to false train trips, included in the STR false train trip rate calculation. ); d. Security Failure Detection (SD): Security failures that can be diagnosed and detected, serving as input for SFF calculation ( ); (2) Classification by failure mechanism (from the perspective of equipment integrity): It is divided into five categories: pressure boundary failure (rupture, leakage), functional failure (blockage, stoppage, jamming), instrument control failure (low output, high output, no response, drift), pressure reducing device failure (early opening, inability to open, inability to reseat) and damage mechanism (corrosion, wear, fatigue, creep, stress corrosion cracking (SCC), hydrogen corrosion; (3) Classified by PERD loop status: divided into interlocking function failure (interlocking failure to operate, interlocking false operation), alarm function failure (alarm rejection, false alarm), abnormal process variable indication (excessive deviation, indication loss) and false discrete indication; standardized coding is output to the data management layer through this layer, and the fields of data acquisition and equipment master data all refer to its classification code.

[0028] The advantages are: by constructing a standardized coding system with three orthogonal dimensions in the field of process safety, namely functional safety (four categories of PDS), equipment integrity (failure mechanism), and loop condition (PERD), this design can solve the fundamental obstacle of "inconsistent terminology and inconsistent classification" when comparing data across enterprises / industries. It enables failure data from different factories and different equipment to be compared and aggregated statistically under a unified coding framework, thus laying a semantic foundation for subsequent multi-organization federated learning and the establishment of industry benchmarks.

[0029] The third data management layer stores and manages device master data, failure event data, inspection data, and audit traceability information, providing structured data support for the computing engine layer. This layer consists of the following sub-modules: (1) Equipment master data management registers equipment according to a three-level equipment classification system: L1 functional category (SIS equipment / process equipment) → L2 equipment group code (pressure transmitter / shut-off valve / logic controller / safety valve / pump / heat exchanger, etc.) → L3 technical type code (differential pressure / Coriolis force / spring return / piston / centrifugal, etc.); equipment master data management pre-builds equipment templates based on the three-level classification system, and new equipment registration can inherit the default parameters of the L3 level (such as typical λ value, SFF experience range, recommended inspection cycle), which reduces duplicate entry while ensuring data consistency; it is divided into six sub-modules according to functional domain: Sub-module A of Identification and Traceability Information includes the device tag number (unique identifier, conforming to ISA 5.1 / P&ID naming convention), manufacturer (referenced from the manufacturer's master data table), model, serial number, firmware version (required for programmable electronic devices), device barcode / QR code, and purchase order number. This sub-module enables quick on-site retrieval via barcode / QR code scanning and precise matching with the device ID in the failure event record, ensuring a one-to-one correspondence between physical devices and digital files. Sub-module B, Hierarchical Attribution and Spatial Location: Includes factory code, unit code, installation location, floor / elevation, GPS coordinates, and P&ID drawing number; Installation and Service Information Submodule C: Includes commissioning date, installation date, expected decommissioning date, cumulative operating hours (automatic calculation or manual input), operating mode (continuous operation / intermittent operation / standby), and average number of start-ups and shutdowns per year; Submodule C supports dual calculation modes for cumulative operating hours: the system automatically calculates from the commissioning date according to calendar time (for continuously operating equipment), or calculates based on the actual operating / downtime in the CMMS work order using a weighted cumulative method (for intermittently operating equipment). Users can select the calculation rule when registering the equipment; Service Conditions Submodule D: Includes medium name, medium phase (gas / liquid / gas-liquid two-phase / containing solid), medium corrosivity level (low / medium / high / severe), design temperature, operating temperature, design pressure, operating pressure, service type (continuous service / intermittent service / emergency standby), and environmental conditions (outdoor / indoor / explosion-proof area / corrosive atmosphere, etc.). Safety Attribute Submodule E: Includes SCE identifier (whether it is a safety critical element), target SIL level (SIL 1 / 2 / 3 / 4 or NR), associated SIF number, SIF loop role (sensor / logic controller / final actuator), MooN voting structure (1oo1 / 1oo2 / 2oo3, etc.), A / B type classification (A type simple device / B type complex device), hardware failure margin (HFT) (0 / 1 / 2), and safety failure score (SFF) (from FMEDA report or automatically calculated by the system). In submodule E, the system implements built-in consistency verification for the target SIL level, hardware failure margin (HFT), and A / B type classification. When the combination of the three does not meet the structural constraints of IEC 61508, an automatic warning is issued, ensuring the compliance of SIL classification from the source. The safety failure score (SFF) can be manually entered from the FMEDA report or automatically calculated by the system from the reliability parameter automatic calculation module, ensuring synchronization with reliability parameter statistics.

[0030] The Changes and Documents Association submodule F includes the MOC association number, MOC date, FMEDA report reference, SRS reference, SIF verification report reference, equipment photos, and notes. (2) Failure data storage: Store all failure event records (including standard fields) from the four acquisition channels of the failure data acquisition module; The failure data storage establishes a connection with the device master data through the device ID as a mandatory foreign key. When each failure record is entered into the database, the system automatically verifies the validity of the device ID and updates the cumulative number of failures and the last failure time of the corresponding device in the device master data in a cascading manner. (3) Inspection data storage: Store wall thickness measurement, NDT non-destructive testing, corrosion monitoring and online monitoring data; The wall thickness reduction and corrosion rate calculation results in the inspection data storage will be automatically fed back to the equipment master data submodule D, triggering dynamic correction of the expected retirement date and reassessment of the remaining service life RL, realizing real-time linkage between inspection findings and equipment service records; (4) Audit traceability: Record the creation, modification and review history of all data, and support full-link traceability; Audit traceability not only records the operator, operation time and operation type, but also records the comparison of the values ​​before and after the field level in detail (such as the PDS category changing from "pending review" to "DU", the failure mechanism changing from "pending analysis" to "stress corrosion cracking"), to ensure that any correction of the failure event classification can be traced back to the original entry decision and review basis, and meet the complete evidence chain requirements of functional safety audit; When the above sub-modules are entered into the database, the system performs strict integrity checks (including checks on non-empty fields, enumeration value ranges, logical compatibility between equipment type and failure mechanism, and time logic rationality) to ensure that the data stored in this layer is complete and consistent. After being processed by this layer, master data, failure events, and inspection data are sent to the computing engine layer in the form of standardized materialized views (such as equipment reliability feature views, failure event detail summary views, and inspection status tracking views) rather than the original business tables. This ensures that the computing engine obtains a structured dataset that has been cleaned, merged, validated, and has complete contextual relationships.

[0031] The fourth-layer computational engine automatically performs reliability parameter statistical calculations, RBI remaining lifetime calculations, and chi-square confidence assessments based on raw failure data, and outputs the calculation results to the application layer. This layer follows a five-stage process: "data filtering and preprocessing → failure count statistics → failure rate parameter calculation → confidence assessment → result output and verification". S1.1 Data Filtering and Preprocessing: Filter out invalid records with audit status = "Confirmed", group them by equipment type (L2 equipment group code), exclude atypical operating condition data (abnormal values ​​during start-up / stop), and calculate the cumulative operating hours EDH for each group; S1.2 Failure Count Statistics: Counted by PDS Category , , , The failure count is classified and counted according to failure mechanism and PERD loop condition, and the failure count matrix of each equipment type is output. S1.3 Failure rate parameter calculation: The core parameter calculation formulas are as follows: (1) The failure rate of undetected hazards is the core input parameter used for PFDavg calculation; (2) Hazard detection failure rate is used as an input parameter for calculating Safety Failure Score (SFF); (3) The safety failure rate is used as an input parameter for calculating the false trip rate (STR); (4) Safety detection failure rate is used as an input parameter for calculating the safety failure fraction (SFF); Derivation of aggregated parameters: Based on the four basic failure rates, the aggregated failure rates for the safe side and the dangerous side are obtained by combining them according to the failure direction, which are then used for higher-level reliability assessments. (5) The total safety failure rate (the sum of failures in all safety directions) is used to assess the upper limit of the STR misjudgment rate; (6) Total hazardous failure rate (the sum of failures in all hazardous directions) is used for Calculation of Mean Time Between Failures (MTBF); (7) The total failure rate (the sum of all failure directions, i.e., the overall equipment failure rate) is used as the denominator in global reliability assessment and SFF calculation; Based on the aforementioned fundamental and aggregated parameters, comprehensive reliability evaluation parameters are further calculated to obtain comprehensive evaluation indices for SIL verification, diagnostic assessment, and reliability decision-making. (8) Safety Failure Score (SFF) reflects the proportion of safe failures and detectable hazardous failures to the total failure rate of equipment, and is the core criterion for structural constraint verification (Route 1H) in IEC 61508: SFF = ,in, Total safety failure rate ( ), For the failure rate of hazard detection ( ), Total failure rate ( SFF∈[0,1], expressed as a percentage (%), is used to combine the equipment A / B type classification with the hardware fault margin HFT, and to look up the table to determine the upper limit of the structural constraint SIL (Route1H). (9) Diagnostic coverage DC∈[0,1], 0 indicates no diagnostic capability, 1 indicates perfect diagnosis, reflecting the proportion of dangerous failures that online diagnosis can capture, used for SIS diagnostic design evaluation, and guiding the direction of improvement of diagnostic methods; (10) Mean time between failures (MTBF) The mean time between failures (MTBF) of equipment, evaluated from the perspective of dangerous failures, is a direct expression of reliability. It is used to intuitively describe the level of reliability, making it easy to compare with the equipment's design life and identify whether it has entered the wear-out and failure period. S1.4 Confidence Assessment: According to GB / T 21109.1, chi-square distribution estimation is used. The upper limit of the confidence interval; key Quantile reference (CL=70%, 0 failure scenario) =2 =2.408): (When r=0); Sample size determination rules: ≥5 indicates acceptable statistical certainty. =1~4 indicates insufficient statistical certainty (confidence intervals are marked). When =0, it cannot be calculated directly. (Only a 70% confidence limit is given); At the same time, the system automatically compares the statistical results with the CCPS LOPA / PDS reference benchmark; S1.5 Result Output and Verification: Output the calculated reliability parameters , , , , , , The upper limit of the 70% confidence interval is output to the device master data security attribute submodule (update). The data (fields) is output to the SIL verification calculator, compared with the design assumptions (a warning is automatically issued if the difference is >20%), and an equipment reliability report is generated. Simultaneously, this layer automatically performs RBI-related calculations based on the test data: (1) Residual corrosion allowance The difference between the current wall thickness and the minimum allowable wall thickness represents the maximum corrosion margin that the equipment can withstand under the current conditions. (Current wall thickness - minimum allowable wall thickness), where, The actual measured wall thickness (mm) is shown. Minimum allowable wall thickness (mm, determined according to design specifications or strength calculations); (2) Corrosion rate CR, the amount of wall thickness reduction per unit time, reflects the severity of the corrosion affecting the equipment: (Difference in wall thickness between two tests ÷ time interval), where, The measured wall thickness (mm) from the previous test. The measured wall thickness (mm) is currently being tested. The time interval between two tests (years); the unit for CR is mm / year; (3) Remaining life RL, which is the remaining time required for the equipment wall thickness to decrease from the current value to the minimum allowable wall thickness at the current corrosion rate, is the core basis for formulating inspection strategies and maintenance plans: (Residual corrosion allowance ÷ Corrosion rate), where RCA is the residual corrosion allowance (mm), CR is the corrosion rate (mm / year); RL is in years; (4) Maximum Inspection Interval (MII): Under the premise of ensuring safe operation of the equipment, the maximum time interval between two inspections is allowed, which is obtained by multiplying the remaining life by the interval factor based on the consequence level. (IF is the interval factor, 0.5~1.0, based on the severity of the failure), where RL is the remaining lifetime (years), and IF is the interval factor (dimensionless, ranging from 0.5 to 1.0, determined according to the severity of the failure consequences, with a more conservative value for more severe consequences). Output description: Raw failure data ( The parameters and RBI evaluation results (RCA, CR, RL, MII) are calculated by this layer and then sent to the application layer. The parameters are directly accessible to the SIL verification calculator, while the RBI results are used for equipment maintenance strategy optimization and inspection plan adjustment. These two types of output complement each other. The parameters serve functional safety SIL verification, while the RBI parameters serve equipment integrity management, together forming a complete data foundation for equipment reliability evaluation.

[0032] The advantages are: by integrating functional safety failure rate statistics (including chi-square confidence interval estimation) with device integrity RBI remaining lifetime assessment in the same computing engine for parallel execution, and by introducing a 70% confidence upper limit as... The conservative estimate solves the problem of small sample size ( <5) Engineering challenges where statistical uncertainty cannot be quantified in the scenario; at the same time, it automatically compares the calculation results with the CCPS LOPA / PDS benchmark, and automatically alerts when the actual failure rate deviates from the design assumption by more than 20%. From the algorithm level, it provides a precise quantitative deviation detection method for the PDCA closed loop of data → SIL verification. RBI calculation and λ statistics share the same set of test data sources, thereby realizing the same source assessment of two types of risks: corrosion thinning and functional failure.

[0033] The fifth layer, the application layer, will calculate the reliability parameters output by the engine layer. , , , , , , The 70% confidence interval upper limit is used for SIL validation calculation, statistical analysis and visualization, and report output, providing direct evidence for decision support. This layer consists of three core functional modules: the SIL validation calculator, the statistical analysis and visualization dashboard, and the report output module. These three modules are connected sequentially according to the collaboration relationship of validation → visualization → output, forming a complete link from data application to decision implementation. The detailed functions of each module are as follows: (1) The SIL verification calculator directly uses / retrieves the output of the fourth layer. The parameters are used to perform dual-dimensional SIL verification. The final SIL is the smaller value between the failure quantity SIL and the structural constraint SIL. The PFDavg / PFH value, SIL level judgment and TI optimization suggestions are output simultaneously. This module is the core tool of the "Check" link in the PDCA cycle. It embeds the actual statistical failure rate back into the design verification process to realize the closed-loop feedback between failure data and SIL verification. (2) Statistical analysis and visualization dashboard: Based on the results of previous calculations, four dashboards are presented in a graphical manner: failure trend analysis, enterprise benchmarking, MI performance indicators and comprehensive dashboard; (3) Report output module: Based on the results of previous calculations, it generates five types of reports: SIL verification report, equipment reliability report, MI performance report, TI optimization report and audit traceability report; The calculation results are output to decision support in the form of structured reports and visual dashboards through this layer, including but not limited to maintenance strategy optimization (developing inspection plans and replacement strategies based on RBI remaining life (RL) and corrosion rate (CR), inspection cycle adjustment (shortening or extending verification test intervals based on TI optimization recommendations), SIL level change decisions (initiating SIF modification or equipment upgrade assessments when actual performance is worse than design assumptions), spare parts planning (optimizing spare parts inventory strategies based on critical equipment failure rate ranking), and PSM audit response (providing complete audit traceability reports as evidence of process safety management compliance), etc. A clear collaborative link is formed between the three modules: the SIL verification calculator is the core analysis engine, the dashboard is the dynamic monitoring interface, and the reports are the static archiving medium. All three share the same calculation criteria and data source (all originating from the output of the fourth layer). (Parameters) to ensure data consistency and logical coherence at every stage from analysis and monitoring to archiving.

[0034] The advantage is that by establishing an automatic comparison mechanism, when the actual... When the deviation from the design value exceeds 20%, the system automatically sends a warning and incorporates a TI back-calculation optimization algorithm with a multi-MooN architecture (including a beta factor). This module fundamentally solves the long-standing industry problem of "actual Proof Test results not being fed back to SIL verification calculation," transforming SIL verification from a static compliance document into a dynamic reliability decision-making tool.

[0035] The SIL verification calculator directly uses / retrieves the output of the fourth layer. Parameters, perform two-dimensional SIL verification: (1) Dimension 1: Failure quantity verification is based on parameters such as MooN architecture, TI, MRT, MTTR, β factor (default 0.05, which can be adjusted according to IEC61508-6 Annex D) and PTC (default 90%). The failure quantity value is calculated according to the PFDavg / PFH formula of the corresponding architecture. The failure quantity SIL is determined by referring to the SIL level table (for low requirement mode, refer to the PFDavg range; for high requirement / continuous mode, refer to the PFH range). SIL Level Table 2 ; (2) Dimension 2: Structural constraint verification is based on SFF, HFT and A / B type classification. The Structural constraint SIL is determined by referring to the Route 1H decision table (IEC61508-2 / GB / T 20438.2).

[0036] Type A Equipment (Simple Equipment) Route 1H Judgment Table 3 ; Type B equipment (complex equipment, including programmable electronics) Route 1H Judgment Table 3 ; Statistical Analysis and Visualization Dashboards: Based on prior calculation results, these dashboards graphically present four main dashboards: failure trend analysis, enterprise benchmarking, performance indicators (MI), and a comprehensive dashboard. Kanban 1: Failure Trend Analysis Kanban includes a failure rate time series line chart (to identify deterioration inflection points), a pie chart composed of DU / DD / SU / SD (to assess diagnostic coverage), a failure mechanism distribution bar chart (to identify major failure mechanisms), a comparison chart grouped by process medium (to identify high-risk media), and a temperature × pressure thermogram (to identify extreme operating condition hotspots). Kanban Board 2: Enterprise Benchmarking Kanban Board with Failure Rate Benchmarking Radar Chart (for various equipment types within the enterprise) (Multi-dimensional comparison with industry benchmarks) Comparison bar chart (actual enterprise) (Blue data compared to industry benchmark gray, with 70% confidence intervals); anonymous data contribution statistics card (number of participating companies, total number of devices, cumulative operating hours); industry benchmark trend line chart; and SIL achievement rate benchmark dashboard; the anonymous data contribution mechanism allows companies to upload only anonymized statistical data (device type + ...). Parameters+ The value is calculated using federal statistical methods to create industry benchmarks without exposing the original failure records. Kanban 3: The MI performance indicator dashboard includes a leading indicator progress bar / dashboard (ITPM on-time completion rate, ProofTest on-time execution rate, MOC closure rate, proactive management, and early intervention for declining leading indicators), a lagging indicator trend line chart (equipment failure frequency, SIF failure count, STR false trip rate, result evaluation, and root cause analysis triggered by rising lagging indicators), a deterioration trend scatter plot + trend line (corrosion rate CR, remaining life RL, wall thickness reduction trend, predictive maintenance: RL < 5 years triggers replacement plan, CR accelerates adjustment of inspection frequency), and a SIF health status card (comparison of the current PFDavg of each SIF with the target value, green light = compliant, yellow light = attention, red light = exceeding the standard). Kanban 4: Integrated cockpit includes a digital card summarizing the total number of devices (total number of devices / SIS devices / process devices), a pie chart summarizing SIL status (distribution of SIFs for each SIL level), and a list of alarms ( The list includes the top 10 non-compliant devices and the SIF list that has not been inspected within the specified time limit (ranked by priority to guide attention to the most pressing issues) and a summary of the statistical period (number of newly added failure records, number of newly completed Proof Tests, λ parameter update date, and 24-hour real-time update indicator to ensure data timeliness).

[0037] The advantages are: by integrating failure rate trends, SIL health, MI performance indicators and enterprise benchmarking into the same visualization platform, and introducing a federal statistical mechanism to achieve anonymous industry benchmarking, enterprises can locate their own reliability level in the industry without exposing trade secrets. The combination of its red, yellow and green light SIF health status card and degradation trend prediction line enables management to quickly identify "which SIFs are at risk and which equipment is about to enter the wear and tear failure period", turning passive response into proactive prevention.

[0038] The report output module generates five types of reports: SIL verification report, equipment reliability report, MI performance report, TI optimization report, and audit traceability report, as detailed below: (1) Report 1: The SIL verification report includes basic SIF information (SIF number, function description, target SIL, MooN architecture, TI, operation mode), equipment list (equipment tag number of each subsystem / Type A / B / HFT), failure quantity verification, structural constraint verification, final SIL determination, actual vs. design comparison and TI optimization suggestions, conclusions and suggestions (text summary, risk warning, rectification suggestions); (2) Report 2: The equipment reliability report includes equipment group overview (equipment group code, equipment quantity N, cumulative operating hours EDH), failure rate statistics, aggregation parameters, confidence analysis, industry benchmark comparison, trend analysis and LOPA data suggestions; (3) Report 3: The MI performance report includes ITPM completion rate (completion rate = number of on-time completions / total planned number × 100%), failure trend (failure frequency = number of failures / statistical period (year), statistical classification by failure mechanism), SIF failure statistics and PSM / AIM compliance (completion status of audit check items); (4) Report 4: The TI optimization report includes SIF overview table, optimization analysis, risk analysis, constraint check (whether TI satisfies MRT<TI<equipment lifetime), priority sorting (TI adjustment priority sorted by the deviation degree of PFDavg, the SIF with the largest deviation is adjusted first); (5) Report 5: The audit traceability report includes the full link of failure events (trace by timeline: equipment registration → failure record → RCA → corrective action → MOC → re-verification), change history (all MOC records of the equipment and corresponding failure parameter changes), SIL verification history (comparison table of previous SIL verification results of the SIF, including PFDavg, SIL determination and TI changes of each verification) and data integrity (marking of missing / abnormal data).

[0039] The advantages are: by providing a five-in-one standardized report system including SIL verification, equipment reliability, MI performance, TI optimization and audit traceability, each report is embedded with key calculation formulas and deviation judgment rules, which realizes the automatic generation of compliance documents from raw data by the system, wherein the two-column comparison design of the SIL verification report and the quantitative assessment of risk increment in the TI optimization report provide a complete traceable evidence chain for PSM audits, which ultimately greatly reduces the document preparation cost and human error risk of functional safety compliance.

[0040] By integrating the SIL verification calculator (analysis engine), visualization dashboard (dynamic monitoring), and report output (static archiving) into a three-in-one module collaboration, the failure data of chemical equipment is transformed from static records for archiving and reference into dynamic decision-making assets that drive SIL verification, guide inspection optimization, and support audit traceability. The three modules share the same set of calculation criteria and data sources (all derived from the fourth-level λ parameter), ensuring data consistency and logical coherence at every stage of the system from analysis and monitoring to archiving, thereby forming a complete PDCA closed-loop management toolchain.

[0041] The system's seven core functional modules are executed sequentially according to the logical chain of data acquisition → standardization → storage → computation → verification → visualization → output. Their specific locations and functions are as follows: The failure mode classification standard library is located in the second-level classification standard layer. It is a lower-level dependency that provides standardized classification code references for fields of data acquisition in the failure data acquisition module and equipment master data in the equipment master data management module. The failure data acquisition module is located in the first data access layer. Based on the failure mode classification standard library, it collects external data through four channels. After collection, the data is entered into the system database. Its failure records provide raw data for the reliability parameter automatic calculation module. The equipment master data management module is located in the third-level data management layer. Based on the failure mode classification standard library (fields refer to its classification codes), it manages equipment master data and provides the automatic reliability parameter calculation module with equipment runtime (EDH) and equipment attribute parameters (MooN structure, HFT, A / B type classification, target SIL, etc.). The automatic reliability parameter calculation module is located in the fourth-layer computing engine layer. Based on the failure records from the failure data acquisition module and the device runtime from the device master data management module, it executes a five-stage calculation process and outputs the results. , , , , , , and the upper limit of the 70% confidence interval; The SIL verification calculator is located at the fifth application layer, directly using / retrieving the output of the reliability parameter automatic calculation module. Parameters, perform failure quantity verification (PFDavg / PFH calculation) and structural constraint verification (Route 1H table lookup), and output the final SIL level and TI optimization suggestions; Statistical analysis and visualization dashboards are located in the fifth application layer, based on the automatic reliability parameter calculation module. The parameters and SIL verification calculator provide SIL verification results, offering a visual display of four dashboards: failure trend analysis, enterprise benchmarking, MI performance indicators, and a comprehensive dashboard. The report output module is located in the fifth application layer. Based on the λ parameter of the reliability parameter automatic calculation module and the SIL verification result of the SIL verification calculator, it serves as a parallel display outlet to generate five types of reports (PDF / Excel format).

[0042] The advantages are: This invention, through a seven-step logical chain of acquisition → standardization → storage → calculation → verification → visualization → output, is the first to systematically map the IEC 61508 functional safety lifecycle (from SRS preparation to SIL verification) with the PDCA quality management cycle at the software architecture level. The output of each step is used as the input of the next step, and deviations in previous steps will be amplified or intercepted in subsequent steps, finally forming a causal closed loop from data generation to decision implementation, thereby establishing a reusable standard process paradigm for functional safety data management in the chemical industry.

[0043] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A chemical equipment failure database system based on a quality management cycle, characterized in that, The system consists of a five-layer technical architecture, seven functional modules, and six scalable modules distributed according to the architectural layers, as detailed below: The five-layer technical architecture consists of a first-layer data access layer, a second-layer classification standard layer, a third-layer data management layer, a fourth-layer computing engine layer, and a fifth-layer application layer. The first-layer data access layer is responsible for acquiring external multi-source heterogeneous raw data through four channels: Proof Test import, CMMS interface, inspection data import, and manual entry. The second-layer classification standard layer is responsible for establishing a unified coding system for equipment classification, failure modes, and PERD loop conditions, providing a standardized semantic foundation for the upper layers. The third-layer data management layer is responsible for the structured storage, multi-dimensional correlation, and audit traceability management of equipment master data, failure events, and inspection data. The fourth-layer computing engine layer is responsible for performing failure rate parameter statistics, chi-square confidence assessment, and PFDavg / PFH reliability index calculation. The fifth-layer application layer is responsible for providing end users with decision support applications such as SIL verification calculation, visualization dashboards, and professional report output. The seven functional modules consist of a Failure Mode Classification (FMCC) standard library, a failure data acquisition module, an equipment master data management module, an automatic reliability parameter calculation module, a SIL verification calculator, and a statistical analysis and visualization dashboard and report output module. The FMCC standard library is the underlying core, and its output PDS failure categories and failure mechanism codes are referenced by the data acquisition and equipment master data modules. The failure data acquisition module, based on the coding rules of the classification standard library, transforms external raw records into standardized failure events through four channels and stores them in the management layer. The equipment master data management module, also based on the classification standard library, registers three-level classifications for each device and accumulates operating hours, providing denominator data for the calculation layer. The automatic reliability parameter calculation module calculates and outputs the failure event counts and operating hours provided by the previous two modules. , Failure rate parameters for SFF and DC; the SIL verification calculator directly retrieves the output from the reliability calculation module. The parameters, combined with the MooN structure, HFT and A / B type classification in the equipment master data, complete the dual-dimensional judgment of failure quantity verification and structural constraint verification; the statistical dashboard and report output are parallel to all calculation results and feed the actual verification results back to the design assumptions, forming a complete closed loop from field testing to SIL verification and then to optimization suggestions; The six expandable modules consist of a mobile data acquisition app, a multi-organizational data federation module, a LOPA integration module, an MOC linkage module, a digital twin interface, and a device reliability digital twin module. The mobile data acquisition app is deployed at the first data access layer, allowing on-site inspection personnel to input failure events via mobile phone photos and voice recording, which are then cached offline and synchronized to the system. The multi-organizational data federation module is deployed at the third data management layer, supporting multiple enterprises to share failure rate statistics without exposing original records through authorized / anonymized calculations, gradually establishing industry benchmarks. The LOPA integration module is deployed at the fifth application layer, backfilling SIL verification results to the Layer of Protection Analysis worksheet with one click and automatically updating the PFD values ​​of independent protection layers. The MOC linkage module is deployed at the third data management layer, interfacing with the change management system to automatically trigger failure risk assessment updates when equipment / processes change. The digital twin interface is deployed at the fourth computing engine layer, accessing DCS / PLC data streams in real time and dynamically updating failure probability estimates. The device reliability digital twin module is deployed at the fifth application layer, fitting the equipment's full lifecycle failure rate curve based on the Weibull distribution to predict future failure trends.

2. The chemical equipment failure database system based on quality management cycle according to claim 1, characterized in that, The first data access layer provides four external data acquisition channels; (1) Channel 1: Import the verification test report template automatically generated by the system based on SRS through the Proof Testing template. It also supports batch import of Excel, PDF OCR recognition and online submission of Web forms. After import, the device master data is automatically associated with the device tag number and updated. / statistics; (2) Channel 2: Supports three methods through the CMMS interface: direct connection to REST API, batch import of CSV / Excel files and timed synchronization of intermediate database. It is used to extract fields including device ID, failure time, failure description, work order number and maintenance duration. The failure category is manually reviewed and confirmed as DU / DD / SU / SD. (3) Channel 3: Supports wall thickness measurement, NDT non-destructive testing report PDF import, corrosion monitoring data import and online monitoring data import through inspection data import, and automatically performs RBI related calculations after import; (4) Channel 4: Manual input includes quick input forms, standard input forms and batch Excel import, and plans to expand mobile voice input; Each failure record, once entered into the database, uniformly includes the following standard fields: record ID, device ID, failure time, data source, test program number, pass / fail result, PERD loop status classification, PDS failure category, failure mechanism, root cause of failure, failure impact, work order number, MOC number, operation mode context, data entry person, and review status.

3. The chemical equipment failure database system based on quality management cycle according to claim 1, characterized in that, The second classification standard layer consists of a classification system with three orthogonal dimensions, as follows: (1) Classification by Failure Consequences - PDS Failure Categories: Failures are classified into four categories based on two orthogonal dimensions: undetected hazardous failures, detected hazardous failures, undetected safe failures, and detected safe failures; the failure rates of the four categories satisfy an additive relationship: = + + + The aggregated parameters include the total safety failure rate. = + Total risk failure rate = + Diagnostic coverage DC = / ( + Safety Failure Score (SFF) = ( + ) / =1- / ; (2) Classification by failure mechanism: It is divided into five categories: pressure boundary failure, functional failure, instrument control failure, pressure reducing device failure and damage mechanism; (3) Classified by PERD circuit status: divided into interlocking function failure, alarm function failure, abnormal process variable indication and false discrete indication.

4. The chemical equipment failure database system based on quality management cycle according to claim 1, characterized in that, The third data management layer is used to store and manage device master data, failure event data, inspection data, and audit traceability information. This layer consists of the following sub-modules: (1) Equipment master data management registers equipment according to a three-level equipment classification system: L1 functional category → L2 equipment group code → L3 technical type code; equipment master data management pre-builds equipment templates based on the three-level classification system, and new equipment can inherit the default parameters of the L3 level when registering, and is divided into the following six sub-modules according to functional domain: Identification and Traceability Information Submodule A: Includes device tag number, manufacturer, model, serial number, firmware version, device barcode / QR code, and purchase order number; Sub-module B, Hierarchical Attribution and Spatial Location: Includes factory code, unit code, installation location, floor / elevation, GPS coordinates, and P&ID drawing number; Installation and Service Information Submodule C: Includes commissioning date, installation date, expected decommissioning date, cumulative operating hours, operating mode, and average number of start-ups and shutdowns per year; Submodule C supports dual calculation modes for cumulative operating hours: the system automatically calculates from the commissioning date according to calendar time, or calculates based on the actual operating / downtime in the CMMS work order using a weighted cumulative method. Users can select the calculation rules when registering the equipment. Service Conditions Submodule D: Includes medium name, medium phase, medium corrosion level, design temperature, operating temperature, design pressure, operating pressure, service type, and environmental conditions; Security Attributes Submodule E: Includes SCE identifier, target SIL level, associated SIF number, SIF loop role, MooN voting structure, A / B type classification, hardware failure margin (HFT), and security failure fraction (SFF). The Changes and Documents Association submodule F includes the MOC association number, MOC date, FMEDA report reference, SRS reference, SIF verification report reference, equipment photos, and notes. (2) Failure data storage: Store all failure event records from the four acquisition channels of the failure data acquisition module. The device ID is used as a mandatory foreign key to establish a relationship with the device master data. When each failure record is entered into the database, the system automatically verifies the validity of the device ID and updates the cumulative number of failures and the last failure time of the corresponding device in the device master data in a cascading manner. (3) Inspection data storage: Store wall thickness measurement, NDT non-destructive testing, corrosion monitoring and online monitoring data; The wall thickness reduction and corrosion rate calculation results in the inspection data storage will be automatically fed back to the equipment master data submodule D, triggering dynamic correction of the expected retirement date and reassessment of the remaining service life RL, realizing real-time linkage between inspection findings and equipment service records; (4) Audit traceability: Records the creation, modification and review history of all data, and supports full-link traceability; Audit traceability not only records the operator, operation time and operation type, but also records the comparison of field-level values ​​before and after.

5. The chemical equipment failure database system based on quality management cycle according to claim 1, characterized in that, The workflow of the fourth-layer computing engine is as follows: S1.1 Data Filtering and Preprocessing: Filter out failure records whose audit status has been confirmed, group them by equipment type, and exclude data with atypical operating conditions; S1.2 Failure Count Statistics: Counted by PDS Category , , , The failure count is classified and counted according to failure mechanism and PERD loop condition, and the failure count matrix of each equipment type is output. S1.3 Failure rate parameter calculation: The core parameter calculation formulas are as follows: (1) Failure rate due to undetected hazards: = ,in, The cumulative number of undetected hazardous failures; EDH represents the cumulative number of equipment operating hours. (2) Hazard detection failure rate: = ,in, This represents the cumulative number of times a hazard detection failure has occurred. (3) Safety failure rate not detected: = ,in, The cumulative number of undetected safety failures; (4) Safety detection failure rate: = ,in, This represents the cumulative number of times a safety inspection has failed. (5) Overall safety failure rate: = + ,in, Failure rate was not detected for safety reasons; (6) Overall risk failure rate: = + ,in, For the failure rate of hazard detection, Failure rate due to undetected danger; (7) Overall failure rate: = + ; (8) Safety Failure Fraction: SFF = ,in, For the overall safety failure rate, For the failure rate of hazard detection, Total failure rate; (9) Mean time between failures: ,in, Total risk failure rate; S1.4 Confidence Assessment: According to GB / T 21109.1, chi-square distribution estimation is used. Upper limit of confidence interval: = ,in Confidence level CL, degrees of freedom Chi-square quantiles, The number of DU failures observed; key Quantile reference: (When r=0) Sample size determination rules: ≥5 indicates acceptable statistical certainty. =1~4 indicates insufficient statistical certainty. When =0, it cannot be calculated directly. Simultaneously, the system automatically compares the statistical results with the CCPS LOPA / PDS reference benchmark. S1.5 Result Output and Verification: Output the calculated reliability parameters , , , , , , The upper limit of the 70% confidence interval is output to the device master data security attribute submodule, and then output to the SIL verification calculator to be compared with the design assumption values ​​and generate a device reliability report. Simultaneously, this layer automatically performs RBI-related calculations based on the test data: (1) Residual corrosion allowance : ,in, This is the currently measured wall thickness. Minimum allowable wall thickness; (2) Corrosion rate CR: ,in, This is the measured wall thickness from the previous test. This is the currently measured wall thickness. The time interval between two tests; the unit of CR is mm / year; (3) Remaining life RL, where RCA is the remaining corrosion allowance and CR is the corrosion rate; the unit of RL is years; (4) Maximum test interval (MII): Where RL is the remaining lifetime and IF is the interval factor.

6. The chemical equipment failure database system based on quality management cycle according to claim 1, characterized in that, The fifth application layer will calculate the reliability parameters output by the engine layer. , , , , , , The 70% confidence interval upper limit is used for SIL validation calculation, statistical analysis and visualization, and report output. This layer consists of a SIL validation calculator, a statistical analysis and visualization dashboard, and a report output module. Specific functions are as follows: (1) The SIL verification calculator directly uses / retrieves the output of the fourth layer. The parameters are used to perform dual-dimensional SIL verification. The final SIL is the smaller value between the failure quantity SIL and the structural constraint SIL. The PFDavg / PFH value, SIL level determination and TI optimization suggestions are output simultaneously. (2) Statistical analysis and visualization dashboards are based on the results of previous calculations and present four dashboards in a graphical manner: failure trend analysis, enterprise benchmarking, MI performance indicators and comprehensive dashboard. (3) The report output module generates five types of reports based on the previous calculation results: SIL verification report, equipment reliability report, MI performance report, TI optimization report and audit traceability report.

7. The chemical equipment failure database system based on quality management cycle according to claim 6, characterized in that, The SIL verification calculator directly uses / retrieves the output of the fourth layer. Parameters, perform two-dimensional SIL verification: (1) Dimension 1: Failure quantity verification is based on the MooN architecture, TI, MRT, MTTR, β factor and PTC parameters. The failure quantity value is calculated according to the PFDavg / PFH formula of the corresponding architecture, and the failure quantity SIL is determined by referring to the SIL level table. (2) Dimension 2: Structural constraint verification is based on SFF, HFT and A / B type classification, and the structural constraint SIL is determined by referring to the Route 1H decision table.

8. The chemical equipment failure database system based on quality management cycle according to claim 1, characterized in that, The statistical analysis and visualization dashboards, based on prior calculation results, present four main dashboards graphically: failure trend analysis, enterprise benchmarking, MI performance indicators, and a comprehensive dashboard. (1) Kanban 1: Failure Trend Analysis Kanban includes a failure rate time series line chart, a pie chart composed of DU / DD / SU / SD, a bar chart of failure mechanism distribution, and a comparison chart grouped by process medium; (2) Benchmarking Board Two: Enterprise Benchmarking Board including Failure Rate Benchmarking Radar Chart, Compare bar charts, anonymous data contribution statistics cards, industry benchmark trend line charts, and SIL achievement rate benchmarking dashboards; among them, the anonymous data contribution mechanism allows enterprises to upload only anonymized statistical data, and calculate industry benchmarks through federated statistical methods without exposing the original failure records; (3) Dashboard 3: The MI performance indicator dashboard includes a leading indicator progress bar / dashboard, a lagging indicator trend line chart, a deterioration trend scatter plot + trend line, and a SIF health status card with red, yellow and green lights. (4) Kanban 4: The integrated cockpit includes a digital card summarizing the total number of equipment, a pie chart summarizing the SIL status, an alarm list, and a summary of the statistical period.

9. The chemical equipment failure database system based on a quality management cycle according to claim 6, characterized in that, The report output module generates five types of reports: SIL verification report, equipment reliability report, MI performance report, TI optimization report, and audit traceability report, as detailed below: (1) Report 1: SIL verification report includes basic SIF information, equipment list, failure quantity verification, structural constraint verification, final SIL determination, actual vs design comparison and TI optimization suggestions, conclusions and recommendations; (2) Report 2: Equipment Reliability Report includes equipment group overview, failure rate statistics, aggregated parameters, confidence analysis, industry benchmark comparison, trend analysis and LOPA data suggestions; (3) Report 3: MI performance report includes ITPM ​​completion rate, failure trend, SIF failure statistics and PSM / AIM compliance; (4) Report 4: TI Optimization Report includes SIF overview, optimization analysis, risk analysis, constraint check and priority ranking; (5) Report 5: Audit traceability report includes the entire chain of failure events, change history, SIL verification history and data integrity.

10. The chemical equipment failure database system based on quality management cycle according to claim 1, characterized in that, The specific locations of the seven functional modules in the system are as follows: The failure mode classification standard library is located in the second classification standard layer. It is a lower-level dependency that provides standardized classification code references for the fields of data acquisition by the failure data acquisition module and equipment master data management module. The failure data acquisition module is located in the first data access layer. Based on the failure mode classification standard library, it collects external data through four channels. After collection, the data is entered into the system database. Its failure records provide raw data for the reliability parameter automatic calculation module. The device master data management module is located in the third data management layer. Based on the failure mode classification standard library, it manages device master data and provides the device runtime and device attribute parameters to the reliability parameter automatic calculation module. The automatic reliability parameter calculation module is located in the fourth-layer calculation engine layer. Based on the failure records of the failure data acquisition module and the device runtime of the device master data management module, it executes a five-stage calculation process and outputs... , , , , , , and the upper limit of the 70% confidence interval; The SIL verification calculator is located in the fifth application layer and directly uses / retrieves the output of the reliability parameter automatic calculation module. Parameters are set to perform failure quantity verification and structural constraint verification, and the final SIL level and TI optimization suggestions are output. The statistical analysis and visualization dashboard is located in the fifth application layer, based on the automatic reliability parameter calculation module. The parameters and SIL verification calculator provide SIL verification results, offering a visual display of four dashboards: failure trend analysis, enterprise benchmarking, MI performance indicators, and a comprehensive dashboard. The report output module is located in the fifth application layer, based on the automatic reliability parameter calculation module. The parameters and SIL verification results from the SIL verification calculator are displayed as parallel outputs, generating five types of reports.

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