The Challenge

Our client, a prominent player in engineering, construction, and Operations & Maintenance (O&M) of wastewater treatment plants (WWTP), faces challenges in operating and maintaining small WWTPs due to cost competitiveness. Serving diverse industrial and government customers in sectors such as breweries, paper mills, sugar mills, soft drinks, towns, and cities, they seek a cost-effective, high-tech solution with Sensfix. The goal is to provide an easy-to-use platform for unskilled workers, facilitating real-time monitoring across diverse stakeholders scattered across different geographies.

In pursuit of this goal, a Proof-of-Concept (PoC) was initiated at an industrial wastewater treatment plant serving a major European beer manufacturer. The PoC aimed to showcase Sensfix’s Do-It-Yourself Digital Maintenance Platform, ensuring 100% digitization of maintenance operations, automating daily O&M procedures, and exploring its potential for smaller plants lacking connectivity, SCADA, and operated by non-skilled workers across three shifts daily.

The Solution

Sensfix implemented its SDM suite module-by-module throughout the Proof of Concept (PoC), employing its flagship three-phase approach: Digitize, Automate, and Optimize.

In the Digitization phase, Sensfix comprehensively understood maintenance workers’ tasks, aligned respective SDM modules to digitize daily operations. A digital twin tree structure rendered on a web UI allowed the plant manager and stakeholders to visualize assets on a map view creating an efficient asset hierarchy – parent asset (WWTP) at the top and child assets (machines and equipment that were operated and maintained on a daily basis) as the first branch.


For laboratory tests, SDM- Formify Pro module was implemented. The solution entailed the dynamic creation of digital form templates on a mobile app, replacing traditional paper forms. These forms catered to both singular and multiple parameters. Maintenance personnel seamlessly inputted data, generating dynamic data forms. During data entry, they could conveniently reference historically entered data in a table below the form. The resulting dataset was visualized through time plots, illustrating the temporal variation of wastewater parameters. Plant managers and relevant stakeholders, equipped with secure login credentials, accessed a user-friendly web UI from any location. This interface allowed them to interactively explore individual parameter plots over time through a simple click-based navigation.

Maintenance personnel utilized the mobile app to capture the readings of digital and analog meters in the WWTP. As these meters lacked connectivity to SCADA or the internet, digitization relied on Optical Character Recognition (OCR) or Computer Vision-enabled image analytics. Leveraging Sensfix SDM-ServiceScanAI module, the maintenance worker effortlessly scanned the asset of interest in a fraction of a second. The AI promptly identified the asset, triggering the automatic launch of a corresponding computer vision model to extract either the desired parameter on an LCD display or a specific characteristic from the image. The output was displayed as a dynamic data form. This form was subsequently plotted on the same Web UI, corresponding to the child asset involved in the process.

Collaborating with IoT partners, Sensfix commissioned motor health sensors that captured various parameters. The data was visualized on the digital twin of motors through the same web UI.
In the Automation phase, Sensfix implemented a multimodal AI-based rule engine. This allowed clients to set rules and thresholds on parameter values, enabling instantaneous alerts and swift first responses. Another user-friendly web UI provided an efficient rule builder for easy customization.

Finally, in the Optimization phase, Sensfix fine-tuned the system, ensuring seamless integration and enhanced decision-making for the client in their wastewater treatment plant operations


The Outcome

The implementation of Sensfix’s AI-enabled SDM suite yielded significant improvements in operational efficiency of the WWTP –

  • Reduced Mean Time To Repair (MTTR) and increased machine uptime.
  • Enhanced first-visit success rates in maintenance tasks.
  • Improved accuracy and efficiency in data management.
  • Lowered equipment maintenance costs.
  • Streamlined workflow, leading to better resource allocation and decision-making.