Favicon of Rockwell Automation

Acciona Agua

Acciona Agua Implements Predictive Maintenance for Water Pumps with Vibration and Temperature Monitoring

The Challenge

Acciona Agua operates drinking water and wastewater treatment facilities where pump reliability is directly tied to public service continuity. Managing pump assets across geographically distributed sites created a significant operational gap: without automated condition monitoring, technicians had to conduct manual inspection rounds to assess pump health — a labor-intensive approach that left abnormal vibration and temperature conditions undetected between visits. In water utility operations, an unplanned pump failure doesn't just disrupt throughput; it can trigger service outages and accelerate costly equipment replacement. The absence of real-time monitoring locked the company into reactive maintenance cycles, shortening asset lifespans and leaving control room operators with limited visibility into the status of critical distributed equipment.

The Solution

Acciona Agua partnered with Rockwell Automation to build an integrated condition monitoring and automated control system across its water treatment infrastructure. Allen-Bradley CompactLogix programmable logic controllers were paired with Dynamix 1444 vibration and temperature monitoring modules, which apply predictive ML algorithms to continuous sensor data to detect anomalies against configurable operating thresholds. Allen-Bradley POINT I/O modules provided distributed I/O connectivity across pump assets, while Studio 5000 software served as the unified programming and configuration environment. When sensor readings exceed defined vibration or temperature limits, the system automatically commands a pump shutdown — moving protective response from periodic human inspection to real-time edge intelligence. This architecture delivers sensor-level monitoring directly within the PLC layer, integrating condition awareness into the existing control system without requiring a separate analytics infrastructure.

Results

The deployment transitioned Acciona Agua from reactive, inspection-driven maintenance to continuous condition-based operations. Machinery lifespan increased as the system detects and responds to abnormal operating conditions before they escalate to mechanical failure or secondary damage. Control room operators gained centralized visibility into pump health across all monitored facilities, substantially reducing the need for manual on-site inspection tours. The automated alerting and shutdown logic also accelerated operator decision-making — teams act on structured condition alerts rather than raw sensor readings. Key operational outcomes include:

  • Extended pump service life through early anomaly detection and pre-failure shutdown
  • Reduced field inspection burden with real-time remote visibility replacing routine site visits
  • Faster incident response driven by automated threshold-based control logic
  • Improved control room situational awareness across distributed treatment assets

Key Takeaways

  • Embedding vibration and temperature monitoring modules directly within the PLC architecture enables edge-level predictive decisions without a separate analytics platform — reducing system complexity and latency.
  • Automated threshold-based shutdown logic protects critical water infrastructure from failure propagation while reducing cognitive load on operators during abnormal conditions.
  • Centralizing condition monitoring for geographically distributed pump assets is achievable with modular, PLC-integrated sensor hardware and minimal additions to existing control infrastructure.
  • In water utilities, predictive maintenance ROI extends beyond cost reduction — pump reliability has a direct public service dimension that justifies investment in continuous monitoring.
  • Establish vibration and temperature baselines under normal operating conditions before configuring shutdown thresholds to minimize nuisance trips during commissioning.

Share:

Details

AI Technology
Predictive ML
Company Size
Enterprise
Quality
Verified

Have a similar implementation?

Share your customer's AI results and link it to your vendor profile.

Submit a case study →