Kognitwin - Predictive Maintenance

**Overview**
Leverage and manage the predictive maintenance tasks for your asset originated from Databricks Lakehouse capabilities including; Delta Live Tables, Unity Catalog, Delta Sharing, MLFlow, AutoML. Kognitwin receives predictive maintenance notifications and events. Inside Kognitwin they are being; Identified, scoped, planned, scheduled, executed, and closed out through one single user interface. Kognitwin pushes/writes back relevant information to Databricks and/or coexistent system to update relevant records, systems, models.

**Use cases**
- Identification and scoping - Detect issues, opportunities for improvements, necessary maintenance tasks from Databricks. Define the extent and objectives of identified work, gather relevant data and outline necessary actions. (1)
- Scope - Refine and detail the initial scope by conducting further analysis, assessing risk, and allocating resources. Develop a comprehensive plan specifying the required actions, responsible parties and timeline. (1)
- Plan - Develop a detailed execution plan; task breakdown, resource allocation, cost estimation and risk management strategies. (1)
- Schedule - Create detailed schedule outlining timing and sequence of tasks. Coordinate with stakeholders to ensure a realistic plan, align with operational constraints and resource availability. (1)
- Execute - Implement the plan performing the scheduled tasks. Monitor progress, manage resources, and address any issues that arise the ensure work is completed efficiently and safely.
- Close-out - Complete all tasks and ensure work meets defined objectives. Conduct final review, document outcomes, and create final report detailing work performed, results achieved, and lessons learned for the future.
- Utilize Co-pilot and cockpits for Awareness Learning and Dynamic Task Management

(1): Careful assessment and considerations of: Criticality, Priority, Constraints, SIMOPS, Operating window, Risk Management, Supply Chain and Inventory, Resource Management

**Benefits**
- Enable decision making based on the real-time and predicted state of the facility
- Downtime avoidance associated with proactive coordination of maintenance tasks
- Optimal asset life cycle management and resource management
- Holistic and centralized overview of all predictive maintenance work not affecting safety of the operation

**Who is it for?**
- Operations Leadership
- Technical Safety / HSE
- Maintenance Planners / Schedulers
- Console / Field Operators
- Operations Coordinator

**Integrations**
- Maintenance System
- Engineering Data Warehouse
- Document Management System
- Risk Register
- Historian
- Real-time (OPC UA/HDA) Data

搜集汇总

背景与挑战

背景概述

该数据集支持预测性维护任务的全生命周期管理，利用Databricks Lakehouse功能从问题识别到执行关闭，帮助用户实现实时决策、减少停机时间并优化资产运营。它适用于运营领导、维护规划师等角色，并能与维护系统、数据仓库等集成。