Revolutionizing Electric Motor Health with an AI-driven Solution

Company: Blueridge.ai

Project Duration: 2019-2020

Role: Lead Product (UX) Designer.

Objective: To develop an AI-powered platform that predicts potential motor failures, reducing unplanned production downtime and consequential financial losses.

Background: Blueridge.AI is a machine-learning company that predicts electric motor failures. With clients such as Sherman Williams and Trex, the aim was to leverage machine learning algorithms and sensor technology to preemptively detect signs of motor failures. Industries face significant financial setbacks with unexpected motor failures, making predictive measures essential.

Key Strategies and Approaches:

• On-site user challenges research.
• Collaborative UX process.
• Regular feedback for iterative design.
• Prototyping using Adobe XD.

Solution:The developed web application showcased a dashboard visualizing motor health with color-coded indicators. The ML-powered system gives users foresight into potential motor failures, integrated with Google's Material Design for a seamless experience. The physical components, like the magnetic holsters, were also refined for data collection.

Impact:The MVP's successful launch attracted multimillion-dollar accounts before 2020. The platform provided industries with pivotal insights, averting potential lost revenue due to unplanned downtimes.

Introduction:

The industrial sector has been notoriously resilient to rapid technological change, often due to its sheer scale, investment, and complexity. One such challenge that industries have grappled with for decades is electric motors' maintenance and efficient functioning. An unplanned motor failure doesn't just halt a production line; it cascades into logistical nightmares, missed deadlines, and significant revenue loss.

Enter Blueridge.ai. Founded on the principle of harnessing the power of machine learning to tackle real-world industrial challenges, Blueridge.AI set its sights on this age-old problem. Our primary clientele, including giants like Sherwin Williams and Trex, sought a solution beyond just monitoring. They needed predictive intelligence – a system that could alert them well before an impending motor failure, allowing preventive action rather than reactive measures.

However, before my involvement, the product was mainly in its conceptual phase. The technology and the algorithms were in development, but there needed to be more in terms of user experience, interface design, and product applicability. Addressing these gaps was not just about aesthetics but ensuring the high-tech solution was grounded in the daily realities of the industries it aimed to serve.

Hypothesis:

The central hypothesis driving our product design and development was two-fold:

1. Predictive Analysis Over Reactive Measures: By leveraging machine learning and vast datasets on motor frequencies, we believed we could create a system to predict motor failures well in advance. This would transition industries from costly reactive measures to proactive, cost-effective maintenance schedules if successful.
2. User-Centered Design is Key: No matter how advanced or accurate our predictive model was, its success hinged on its usability on the factory floor. We posited that a platform aligned with the shop floor's daily challenges and operational nuances, built upon a foundation of Google's material design for rapid development, would enhance user adoption and satisfaction.

Our hypothesis wasn't just about proving the viability of our tech but ensuring that our end-users, from technicians to plant managers, could seamlessly integrate our solution into their daily workflows. We could truly revolutionize electric motor maintenance by matching our advanced AI capabilities with an intuitive, user-friendly design.

Approach & Methodology:

Since Blueridge.ai was just beyond their seed round funding, there needed to be some foundational work to make sure the product development cycle pushed forward and innovated.

• On-Site Immersion: To deeply understand the challenges faced by our users, I embarked on several on-site visits to client locations. These visits offered invaluable insights into the real-time issues faced on the production lines and the tangible impacts of motor failures.
• Product Design from the Ground Up: Started with hand-drawn sketches, visualizing the user interface and overall application flow. Transitioned from sketches to fully interactive Adobe XD prototypes, allowing users and stakeholders to get a feel of the application.  

• ‍Feedback Iteration: Regularly gathered feedback on these prototypes, made refinements, and provided clients with updated, clickable versions. This iterative approach ensured the design met user requirements while aligning with business goals.‍
• Stakeholder Collaboration: A close-knit collaboration with the CEO, Director of Product, and other critical stakeholders was established. We engaged in extensive feature mapping, prioritization, and user journey mapping exercises, ensuring that our product roadmap was in sync with both user needs and investor expectations.

• Leveraging Google's Material Design: Recognizing the need for a swift development cycle and reduced tech debt, we anchored our design strategy around Google's material design. This provided a structured framework, enabling faster iterations and a cohesive user experience.

• Iterative User Feedback: Regular check-ins with current clients played a pivotal role. Their feedback on the digital platform's pros, cons, and potential areas of improvement was invaluable. Based on this feedback, I would iterate on our designs, offering updated prototypes created with Adobe XD, allowing users to interact with the revamped features.
• Data Visualization Emphasis: The heart of our solution was in effectively communicating complex machine learning predictions. I delved deep into data visualization principles, sketching initial ideas and concepts before translating them into digital mediums. This ensured our dashboard was not just informative but intuitive.

• Agile Development & Scrum: Adopting an agile methodology, I spearheaded daily scrums and sprint planning sessions. This approach ensured our design, development, and strategic pathways remained aligned, and we could swiftly pivot based on new insights or changing priorities.

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This structured and user-centric approach was foundational in steering our product from a conceptual stage to a viable MVP, ready for market deployment.

Solution & Finalized Design:

Our solution culminated in the development of an elegant and intuitive web application. Central to our platform was a unified dashboard that provided a transparent, holistic view of all motor health indicators. With the integration of symbol and color-coded markers, it became instantly evident which motors operated optimally and which were at risk.

Leveraging the power of machine learning, the system could predict potential motor failures, offering users advanced notice—sometimes days or weeks ahead. This predictive approach drastically minimized unplanned downtime and the consequent financial implications. One of the standout features of our solution was the interactive data visualization. Rather than presenting users with static charts, we designed them to be interactive, empowering users to delve deeper into anomalies or patterns and ensuring they had all the pertinent information to make informed decisions.

In terms of design, we seamlessly integrated Google's Material Design principles, striking a balance between consistency and innovation. This choice enhanced user experience and streamlined our development process, enabling a swift product rollout. Beyond the realm of the digital interface, our focus extended to refining the designs for our physical magnetic holsters, ensuring they were effective in data collection and robust enough to handle challenging industrial environments.

Post-launch, we didn't stop there. We instituted a continuous feedback mechanism with our clients. This ensured that regular updates, based on their real-world experiences, were integrated into the platform, making sure our tool remained aligned with the evolving user needs and industry standards. The result was a groundbreaking product that not only met but also exceeded our initial MVP standards, providing industries with an invaluable asset to ensure seamless operations.