通过智能维护改变水管理

水对我们的生存至关重要，但它已成为地球上最稀缺的自然资源之一.

人口增长的关系, industrial development and climate change has presented the world with the unprecedented challenge of ensuring sufficient water access to sustain urban demand, as well as supporting the emergent development of renewable energy technologies and growing agricultural requirements.

伍德的使命是通过技术驱动为我们的世界创造一个水安全的未来, sustainable solutions; drawing on our global network of expertise to apply innovative solutions to the regionally discrete challenges of water security.

在全球范围内，Wood与水务部门有着长期的创新联系. 在澳大利亚, 我们有超过20年的经验支持当地的水和废水部门. We are committed to 重要aining and improving local infrastructure to meet the demands of modern water networks amid growing urbanization and agricultural development.

Water in Australia is an even more precious resource since the average annual rainfall is just 470mm and is further forecasted to experience climate change* impact. 随着城市的发展, 必须同时开发更智能和更可持续的水管理解决方案. 不断增长的需求与减排目标之间的冲突, treatment and distribution efficiencies are rapidly becoming one of the most crucial requirements for water utilities around the country.

Wood与多家澳大利亚水务运营商的合作关系以及全球跨行业经验, 意味着我们 可扩展的智能维护解决方案 是否可以针对当前维护能力和数据成熟度的所有级别. 拥有端到端的专业知识, our solutions are applied with a consistent focus on developing industry leading practices based on global manufacturing, 生产和分销技术.

通过我们对维护数据采集和管理的基本障碍的深刻理解, Wood’s smart 重要enance solutions incorporate our k现在ledge of aggregated municipal supply assets to ensure that leading technology is applicable to the bespoke needs of our water utility partners, 降低成本，提高资产可靠性.

智能维护解决方案中数据结构的重要性

Obstacles to efficient data structures for contemporary water organisations are often associated with the historic amalgamation of asset ownership and the difficulty of managing multiple control systems, 项目流程和优化方案.

A key aspect to the successful implementation of intelligent solutions is the effectiveness of asset and operational data structures for water utilities.

Wood has conducted comprehensive studies of data maturity and operational processes amongst our water industry partners and developed a systematic approach to ensuring that study findings and optimisation theories can be fully operationalised through our 重要enance delivery partnerships. 创建一系列智能解决方案, 我们为客户的水维护策略提供有效的决策.

A core premise of our smart 重要enance approach is that the default medium for improvement is not the immediate implementation of new technology, 而是利用现有技术来提供高级分析. New technologies are then identified through current state and gap analysis of optimised infrastructure to ensure suitable targeting of capital investments.

A common finding within Wood’s smart 重要enance studies is the untapped potential of underutilised Computerised Maintenance Management Software (CMMS) functionality. Our application and understanding of the most effective deep learning architectures for water infrastructure has resulted in benefits of over 20% direct reduction in 重要enance costs, 间接收益包括立即减少备件库存和提高资产可靠性.

为水务行业实施机器学习和智能

Advancements in machine learning and artificial intelligence have unlocked digital transformation in the water industry. The expanse of artificial neural network applications has surpassed many of the limitations of traditional data-driven decision-making and optimisation for water operations. 随着深度学习能力达到新的高度, 水务行业是理想的受益者，其应用包括:

• 动态资产管理程序，包括优化的维护干预计划
• 可扩展的资产性能和状态监控
• 网络当前状态估计
• 物理和表观水分损失的数值探测
• 节约能源，减少碳足迹
• 制定应急供应计划和协议，协助停工和资本项目交付
• 备件消费模式识别和需求预测

Artificial intelligence is also playing a pivotal role in intelligent business analytics through multi-source data integration, 利用云技术和商业智能可视化进行实时洞察和分析. When implemented in parallel with optimisation initiatives targeting enhanced water operations models and asset 重要enance, these generalised business intelligence 的见解 allow for live identification of opportunistic reallocation of resources, 实现优化的维护效益.

Machine learning and artificial intelligence also create opportunities for self-propagated data structures to develop, 使用外部变量(如日历事件)支持高级趋势预测, 社会经济发展, 还有气候变量. 这在整个数据成熟度曲线中创建了一致的价值驱动因素, with practical benefits possible from basic structure and management development through to advanced analytics and integration.

Wood’s augmented machine learning development has yielded reductions in manual analysis of CMMS and sensor data by up to 90%, 超越大数据分析的局限. 这些业务投资的减少使投资能够迅速获得回报, 为大规模资产基础的优化提供信心.

伍德的智能维护解决方案的实施为我们的行业合作伙伴带来了显著的成果.

优化库存 through identification and optimisation of critical stock keeping units to support asset availability has resulted in approximately 40% reduction in critical inventory value and associated operational costs, 以及维护策略优化, 生产成本降低超过25%，在高临界资产中高达45%. 进一步支持可持续发展目标, energy and fuel consumption reductions of 10% have also been achieved through optimised operating philosophies.

加上质量效益，包括提高资产可靠性, 识别数据完整性和结构改进机会, 维护性能可见性, our smart 重要enance approach drives 重要enance and operational cost efficiencies whilst supporting supply certainty and reliability in the face of growing water scarcity.

尽管澳大利亚的水务公司在处理和分配过程上差别很大, 水安全的目标没有改变.

Creating value within data programs from inception through smart 重要enance is the key to streamlining water management. 这, along with the integration of global inter-industry expertise and the unique 的见解 of success drivers amongst our water utilities partners, unlocks the solution to delivering optimum asset reliability towards providing a water secure future for not only Australia, 但是这个世界.

* http://www.nationalgeographic.com/environment/article/partner-content-how-australia-is-securing-its-water-future