关于

行为经济学与人类体验的交汇点,由此诞生了 Renascence 咨询公司。

NOW HIRING

加入我们的团队,一起重塑世界体验品牌的方式。

查看开放岗位 →

COMPANY

GROW WITH US

CONNECT

服务

为企业品牌提供全面的客户体验和管理咨询。

ALL SERVICES

探索 Renascence 提供的全方位客户体验和管理咨询服务。

浏览所有服务 →

CORE

SPECIALIST

解决方案

转化为可衡量的成果。" is smooth, authoritative, and perfectly captures the source meaning and tone.通过结构化解决方案,将 CX 愿景转化为可衡量的成果。

ALL SOLUTIONS

探索我们提供的所有 CX 解决方案。

浏览解决方案 →

STRATEGY & GOVERNANCE

DESIGN & DELIVERY

CULTURE & EXPERIENCE

行业

跨越十年的客户体验转型,赋能本地区最具代表性的行业。

ALL INDUSTRIES

了解我们如何服务各大行业。

浏览行业 →

BUILT ENVIRONMENT

FINANCE & TECH

PEOPLE & MOBILITY

产品

Renascence专有的工具、平台和AI,赋能客户体验转型。

ALL PRODUCTS

探索 Renascence 的完整产品生态。

浏览产品 →

AI & TECHNOLOGY

LEARNING & GAMES

PLATFORMS & TOOLS

AI PRODUCTS

Opinion

Insights, research, and conversations at the frontier of CX.

ReadExperience JournalArticles & research on CX, behavior, and transformation.Watch & listenExperience LoomOur video podcast on CX & behavior.CuratedCX NewsIndustry news that matters in CX, minus the noise.

Latest articles

Latest episodes

Latest news

中心

免费工具、模板和资源,助您提升客户体验实践。

NEW · MANIFESTO

破釜沉舟。十大美德。绝无借口。——阅读我们为勇敢的咨询顾问撰写的宣言。

开始阅读 →

AI TOOLS

FREE TOOLS

LEARNING

CULTURE

Case Study · Technology Customer Experience & Digital Transformation

利用机器学习和人工智能预测球磨机过载并进行预防

我们开发了一套机器学习系统,可在球磨机过载前30-40分钟进行预测,让操作员有充足的时间和可见性来调整磨机负荷和运行参数。客户此前每年停机9.1天,能耗过高。新的预测模型显著稳定了生产周期,将停机时间减少了30%,并将能耗从530千瓦时降至390-420千瓦时。

Predictive MaintenanceMachine LearningIndustrial Optimization
Client
Confidential (UK Mining & Metals Operator)
Industry
Technology Customer Experience & Digital Transformation
Date
2025-12-12
Timeline

01The Impact

The results, up front.

Downtime Reduction
30%
Improved efficiency via reduction of downtime
Energy Reduction
16%
From 530 kWh to 390–420 kWh
Annual Impact
$0.85M–$1.16M
Bottom line costs reduction achieved

30% reduction in downtime Energy usage decreased from 530 kWh to 390–420 kWh Annual impact of $0.85M–$1.16M Improved mill stability and operator confidence More predictable and efficient production cycles

02 — The Challenge

Where they started.

The mill operation lacked reliable predictive visibility. Operators had to react based on lagging indicators, leading to sudden overloads, emergency shutdowns, and increased wear. Energy consumption remained very high due to instability in mill loading. We started by analyzing sensor data, energy profiles, operating modes, and historical overload events. Our team engineered features across vibration, torque, feed rate, ball-loading, and acoustic signatures to build a robust predictive dataset. A supervised ML model was trained to identify precursor patterns signaling overload conditions. Once deployed, the model continuously monitored live streams and triggered operator alerts 30–40 minutes in advance through an integrated dashboard. We also optimized operating regimes by identifying ball-loading configurations that reduce overload probability. The project combined data science, operations optimization, and real-time monitoring to deliver sustained performance improvements.

04 — Approach & Methodology

How we got there.

The ML system surfaced hidden variable correlations previously impossible to detect manually. Overloads strongly correlated with specific amplitude behaviors in vibration signals and certain ball-loading ranges. Predictive insight enabled proactive load adjustments and optimized mill operating modes.

With the overload risk dramatically reduced, operators transitioned from firefighting to proactive cycle management — stabilizing output and reducing both energy intensity and mechanical stress.

05In Practice

Project samples.

Confidential (UK Mining & Metals Operator) project sample 1

Keep exploring Renascence

Your turn

Ready to write your own success story?

Book a discovery call and see what behavioral CX can do for your business.