Reproducing subjective judgments in complex equipment maintenance into structured data with reproducibility (for high-temperature processes / incineration, exhaust gas, and heat recovery equipment).
AI-FTA is a solution that supports the narrowing down of failure causes and the structuring of judgment in manufacturing and plant environments. It integrates on-site data such as work standards, failure reports, and inspection histories, transforming complex causal relationships into easily understandable structural information. It addresses challenges faced by equipment manufacturers, maintenance departments, and plant operators, such as "dependency on veterans" and "time-consuming cause identification," and supports the standardization of reproducible judgment processes.
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basic information
■ Features Integration of on-site data → Structured decision-making Utilization of past failure cases and causal relationships Acceleration of primary cause estimation Reduction of veteran dependency and support for horizontal deployment Utilization of existing equipment and existing data (no modifications required) * Data linkage with OT / MES / CMMS, etc. is also possible ■ Expected Implementation Effects Improvements such as the following are anticipated: Reduction in time for narrowing down failure causes Elimination of individual dependency in abnormal response and sharing of know-how Standardization of maintenance criteria and horizontal deployment across sites Reduction of equipment downtime risk Assetization of trouble response history (Contributes to the optimization of maintenance for industrial and plant equipment) ■ Applicable Targets (Examples of Equipment) The following types of equipment can be supported: High-temperature continuous processing equipment Incineration equipment (combustion, ash processing, exhaust gas systems) Heat recovery / boiler systems Exhaust, denitrification, catalytic reaction towers Conveying and pre-treatment equipment Multi-system linked equipment (manufacturing and general plants) * Effective in equipment maintenance sites with complex causal relationships.
Price range
P6
Delivery Time
※2-6 months
Applications/Examples of results
■ Performance and Verification Examples Verification conducted in an operational plant management company in Japan (*Customer name and facility name are confidential) Verification focused on continuous processing equipment that has been operational for over 10 years Integrated implementation of failure reports, work standards, and inspection history Structured over 1,200 records Implemented multiple system classification and cause candidate suggestion functions Confirmed the possibility of horizontal deployment across locations *The verification examples are utilized as "effective judgment structure models in the field." ■ Common Concerns (FAQ Style) Q: What is needed before implementation? A: You can start simply by organizing and providing your current maintenance and failure report documents. Q: How much effort is required for implementation? A: It varies depending on the data preparation status of the target systems, but you can start with a Proof of Concept (PoC). Q: In what kind of environments is it effective? A: It is effective in equipment maintenance environments where the causal structure is complex and relies on veteran judgment.
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Company information
Our company is a specialized store focused on LED lighting and custom solutions. We can accommodate OEM products for LED lighting in small lots. Additionally, regarding plant growth, we offer proposals tailored to current needs, including mobile container farms, compact growth systems, and custom growth shelves. We also provide lighting that meets JIS8006 standards for marine applications, as well as explosion-proof lighting. Furthermore, we have lighting fixtures that can be used in ultra-low temperature environments and types with emergency light functions. Please feel free to contact us for inquiries about LED lighting and plant growth.
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