Enabling Smart Maintenance: An IoT & AI-Driven Transformation for Commercial Equipment Operations

The client is a global leader in the design and manufacturing of commercial laundry equipment. With operations spanning over 100 countries, they serve key sectors including laundromats, hospitality, healthcare, and multi-housing. Renowned for their innovation and quality, the client’s products are widely used in both urban and remote markets.

Challenge:

The client aimed to enhance its after-sales value proposition by deploying a predictive maintenance and performance monitoring platform for its commercial washers and dryers. The goal was to reduce downtime at customer-operated laundromats and improve the operational efficiency of machines using real-time IoT data and machine learning.

Solutions

ALTEN implemented a scalable, cloud-enabled IoT and ML solution with the following key components:

Edge Integration: Data was captured from each washer and dryer via on-device edge sensors. These sensors tracked key operational metrics in real time
Cloud Infrastructure on AWS: A custom cloud gateway received and processed edge data. Storage and compute infrastructure were built on AWS to ensure high availability and security.
Data Analytics & Visualization:
- 24-Hour Machine Monitoring: Real-time dashboards displayed the number of machines in Running, Idle, and Error states by hour.
- Operational Efficiency Reports: KPIs such as machine usage patterns, total runtime, idle periods, and fault frequencies were visualized.
- Failure Pattern Detection: The system tracked conditions that commonly led to failures, including lint cleanup delays and deviations in cycle sub-stages like drying, heating, filling, and draining.
Predictive Maintenance via ML:
- Regression-based models correlated historical fault data with current machine behavior to predict failures.
- Trends and anomalies were identified to support preventive maintenance actions.
Web-Based Platform: A user-friendly interface provided access to real-time dashboards, historical performance data, and downloadable reports.

Benefits

Minimized Machine Downtime: Predictive insights enabled proactive maintenance, reducing the risk of unscheduled outages.
Improved Operational Visibility: Laundromat operators gained full visibility into machine health and efficiency across locations.
Faster Issue Resolution: Root cause analysis through anomaly detection reduced diagnostic and repair time.
Increased Customer Satisfaction: Consistent uptime and operational transparency helped the client deliver superior value to their end-users.
Differentiated Market Offering: The ML-driven IoT platform positioned the client ahead of competitors by transforming traditional machines into smart, connected assets.

Knowledge Where It Counts

Delivered real-time visibility into machine performance, enabling faster and more accurate decision-making.
Identified hidden failure patterns early, helping prevent breakdowns before they occurred.
Improved maintenance planning through data-backed predictions, reducing downtime for laundromat operators.
Enhanced after-sales support with actionable insights rather than reactive troubleshooting.
Provided customers with clear, measurable value through increased uptime and optimized machine performance.

Tools & Technologies Used

Edge Sensors & IoT Devices for real-time data capture
AWS Cloud Services for data ingestion, storage, and compute
Custom Cloud Gateway for secure data transfer from edge to cloud
Data Analytics Dashboards for monitoring and reporting
Machine Learning Models (regression and anomaly detection) for predictive maintenance
Web-Based Platform for user access to dashboards and historical insights