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phm-project

Prognostic and Health Management Design

Optimized UX to enhance monitoring, improve usability, and streamline predictive maintenance, reducing downtime and improving factory operations through intuitive data access.

Overview

To boost PHM system performance, both a UX overhaul and new feature development were carried out. The initiative addressed pain points like complex interfaces and slow diagnostics, while introducing AI-powered anomaly detection, real-time alerts, intuitive dashboards, and one-click sensor pairing—resulting in faster deployment, smoother workflows, and improved decision-making.

✅ Result

Redesigned UX flow with AI diagnostics, real-time alerts, and automated sensor pairing reduced deployment and maintenance steps from 6 to 4, enabling faster issue resolution. Improved monitoring and decision-making enhanced productivity, stability, and overall operational efficiency.

🎯 Objective

Enhance PHM (Prognostics and Health Management) efficiency by optimizing UX for various user roles to enable predictive maintenance, minimize unplanned downtime, and streamline troubleshooting.

  • Streamline workflow for quicker decisions and fixes

  • Optimize dashboard usability for clear anomaly detection

  • Improve information hierarchy for better clarity and ease of use

⚠️ Challenge

Legacy systems featured unclear interfaces, complicated failure diagnosis, lack of real-time monitoring, and cumbersome system pairing, all of which led to slower maintenance processes and reduced efficiency.

What is Prognostic and Health Management (PHM)

PHM (Prognostics and Health Management) is a maintenance approach that uses sensors, data analytics, and AI to predict equipment health, detect anomalies early, and optimize maintenance.

It's like a "health predictor" for machines! Imagine if your car could tell you, "Hey, my brakes are wearing out, fix me before I break down!" That’s exactly what PHM does for industrial equipment. By using sensors, data analysis, and AI, it monitors machines, detects potential issues early, and predicts failures before they happen. This helps prevent sudden breakdowns, reduce downtime, and keep everything running smoothly!

ASUS AISPHM →

🚀 Let's now delve into the detailed process of execution

Understanding

👷Users,
Factory Operators & Maintenance Teams

Before designing the system, we conducted field research with factory staff to uncover role-specific challenges. By defining user personas based on workflows, we ensured the system meets industrial needs.

🏭 Environment,
Factory Workflow & Sensor Deployment

Factory environments are not like offices—they are noisy, have fluctuating lighting, and require quick, hands-free interactions. Ignoring these factors leads to frustrating and inefficient designs. It was discovered that the current process differs from their expectations. They want the workflow to better align with on-site needs, improve efficiency:

  1. Office Preparation & Data Entry – Create equipment records in the PHM system, assign IDs, and configure AI monitoring rules.

  2. Site Condition Assessment – ​​Evaluate environmental factors, power stability, and physical constraints for installation.

  3. Sensor & Machine Matching – Attach sensors based on X/Y/Z axes, ensure proper placement, and verify connectivity.

  4. System Integration & Final Testing – Connect sensors to the PHM system, calibrate data accuracy, and validate real-time monitoring.

User Needs & Challenges

  • The dashboard design must be role-specific, allowing users to switch between different perspectives easily.

  • Sensor data should be structured to avoid redundancy and ensure clarity.

  • The sensor connection does not match the workflow, optimize the connection process between sensors and machines to ensure seamless data collection and system integration.

User Needs & Challenges

  • The dashboard design must be role-specific, allowing users to switch between different perspectives easily.

  • Sensor data should be structured to avoid redundancy and ensure clarity.

  • The sensor connection does not match the workflow, optimize the connection process between sensors and machines to ensure seamless data collection and system integration.

User Needs & Challenges

  • The dashboard design must be role-specific, allowing users to switch between different perspectives easily.

  • Sensor data should be structured to avoid redundancy and ensure clarity.

  • The sensor connection does not match the workflow, optimize the connection process between sensors and machines to ensure seamless data collection and system integration.

Competitive analysis

After identifying key pain points in factory environments and workflows, a competitive analysis was conducted to evaluate existing releated systems. By benchmarking industry solutions, we gained insights into their strengths and weaknesses, guiding improvements to better align our system with real-world operational needs.Through benchmarking industry systems, several key insights emerged:

  1. Real-Time Equipment Monitoring & Alerts: Displays key parameters (temperature, vibration, energy) with ISO 10816-based (Vibration severity standard)  alerts, enabling quick anomaly detection and response.

  2. Multi-Level Data Visualization: Offers a global overview, detailed equipment insights, and trend analysis, supporting informed decision-making.

  3. Optimized Layout: Follows a top-to-bottom, left-to-right hierarchy, ensuring critical information is prioritized for clarity and efficiency. to switch between different perspectives easily.


The following section presents a breakdown of our competitive analysis results

🛠️ Project Task I

Enhancing PHM System Efficiency in Machine and Sensor Integration

⚠️ Problem Overview

Field testing of the current PHM (Predictive Health Monitoring) system revealed several pain points that impacted setup efficiency and system reliability:

  • Rigid configuration flow – The machine-to-sensor setup process was inflexible, leading to user frustration during configuration.

  • Asynchronous data updates – Delayed sync disrupted real-time monitoring and predictive maintenance.

  • Complex installation – Cumbersome wiring caused increased setup time and a higher risk of error.

🎯 Design Decision 1: Streamlining Machine-Sensor Integration

To improve usability and setup flexibility, we redesigned the system configuration workflow in collaboration with field operators and R&D teams:

  • Decoupled setup flow – Machines and sensors can now be added independently, removing unnecessary setup constraints.

  • Tree-structured sensor layout – A drag-and-drop interface organizes sensors into a clear, hierarchical structure

  • Reduced cognitive load – Grouped functions and progressive disclosure create a more intuitive, focused navigation experience.

📊 Design Decision 2: Enhancing Sensor Monitoring and Data Visibility

Refined the monitoring interface to provide real-time clarity and ensure system stability:

  • Status indicators with tooltips – Color-coded states (e.g., active, sleep, disconnected) provide immediate visual feedback.

  • Live data sync status – Timestamped sync indicators and a progress bar enhance data visibility and transparency.

  • Simplified navigation – Logical grouping and progressive disclosure streamline user interactions and reduce complexity.

➕ Add Sensor Flow:

Create a seamless experience that minimizes friction while guiding users through sensor setup with clarity

🛠️ Project Task I

Enhancing PHM System Efficiency in Machine and Sensor Integration

⚠️ Problem Overview

Field testing of the current PHM (Predictive Health Monitoring) system revealed several pain points that impacted setup efficiency and system reliability:

  • Rigid configuration flow – The machine-to-sensor setup process was inflexible, leading to user frustration during configuration.

  • Asynchronous data updates – Delayed sync disrupted real-time monitoring and predictive maintenance.

  • Complex installation – Cumbersome wiring caused increased setup time and a higher risk of error.

🎯 Design Decision 1: Streamlining Machine-Sensor Integration

To improve usability and setup flexibility, we redesigned the system configuration workflow in collaboration with field operators and R&D teams:

  • Decoupled setup flow – Machines and sensors can now be added independently, removing unnecessary setup constraints.

  • Tree-structured sensor layout – A drag-and-drop interface organizes sensors into a clear, hierarchical structure

  • Reduced cognitive load – Grouped functions and progressive disclosure create a more intuitive, focused navigation experience.

📊 Design Decision 2: Enhancing Sensor Monitoring and Data Visibility

Refined the monitoring interface to provide real-time clarity and ensure system stability:

  • Status indicators with tooltips – Color-coded states (e.g., active, sleep, disconnected) provide immediate visual feedback.

  • Live data sync status – Timestamped sync indicators and a progress bar enhance data visibility and transparency.

  • Simplified navigation – Logical grouping and progressive disclosure streamline user interactions and reduce complexity.

➕ Add Sensor Flow:

Create a seamless experience that minimizes friction while guiding users through sensor setup with clarity

🛠️ Project Task I

Enhancing PHM System Efficiency in Machine and Sensor Integration

⚠️ Problem Overview

Field testing of the current PHM (Predictive Health Monitoring) system revealed several pain points that impacted setup efficiency and system reliability:

  • Rigid configuration flow – The machine-to-sensor setup process was inflexible, leading to user frustration during configuration.

  • Asynchronous data updates – Delayed sync disrupted real-time monitoring and predictive maintenance.

  • Complex installation – Cumbersome wiring caused increased setup time and a higher risk of error.

🎯 Design Decision 1: Streamlining Machine-Sensor Integration

To improve usability and setup flexibility, we redesigned the system configuration workflow in collaboration with field operators and R&D teams:

  • Decoupled setup flow – Machines and sensors can now be added independently, removing unnecessary setup constraints.

  • Tree-structured sensor layout – A drag-and-drop interface organizes sensors into a clear, hierarchical structure

  • Reduced cognitive load – Grouped functions and progressive disclosure create a more intuitive, focused navigation experience.

📊 Design Decision 2: Enhancing Sensor Monitoring and Data Visibility

Refined the monitoring interface to provide real-time clarity and ensure system stability:

  • Status indicators with tooltips – Color-coded states (e.g., active, sleep, disconnected) provide immediate visual feedback.

  • Live data sync status – Timestamped sync indicators and a progress bar enhance data visibility and transparency.

  • Simplified navigation – Logical grouping and progressive disclosure streamline user interactions and reduce complexity.

➕ Add Sensor Flow:

Create a seamless experience that minimizes friction while guiding users through sensor setup with clarity

Project Task 2

Dashboard Optimization

⚠️ Problem Overview

The existing dashboard was designed as a one-size-fits-all interface, serving three distinct user roles—monitoring personnel, maintenance teams, and analysts—but providing the same level of detail to everyone, leading to unnecessary complexity:

  • Rigid configuration flow – The machine-to-sensor setup process was inflexible, leading to user frustration during configuration.

  • Asynchronous data updates – Delayed sync disrupted real-time monitoring and predictive maintenance.

  • Complex installation – Cumbersome wiring caused increased setup time and a higher risk of error.

🎯 Design Decision: Tailored, Role-Based Dashboards

To address these issues, we partnered with field operators and R&D to redesign the dashboard with a role-specific, visual-first approach that prioritizes clarity and actionability:

  • Visual alert focus – Replaced text-heavy data with a 10816 color grid indicator to distinguish normal (green) and abnormal (red) machines at a glance.

  • Reduced visual clutter – Replaced excessive numerical cards with clear, icon-based visuals and summarized status indicators.

  • Anomaly-driven navigation – Enabled direct click-through from any alert to detailed machine or sensor logs, reducing time-to-troubleshoot.

  • Role-based dashboards – Users only see information relevant to their tasks:

    • 👤 Monitoring staff see a high-level alert view

    • 👤 Maintenance teams access issue-specific diagnostics

    • 👤 Analysts can explore deeper performance metrics

Project Task 2

Dashboard Optimization

⚠️ Problem Overview

The existing dashboard was designed as a one-size-fits-all interface, serving three distinct user roles—monitoring personnel, maintenance teams, and analysts—but providing the same level of detail to everyone, leading to unnecessary complexity:

  • Rigid configuration flow – The machine-to-sensor setup process was inflexible, leading to user frustration during configuration.

  • Asynchronous data updates – Delayed sync disrupted real-time monitoring and predictive maintenance.

  • Complex installation – Cumbersome wiring caused increased setup time and a higher risk of error.

🎯 Design Decision: Tailored, Role-Based Dashboards

To address these issues, we partnered with field operators and R&D to redesign the dashboard with a role-specific, visual-first approach that prioritizes clarity and actionability:

  • Visual alert focus – Replaced text-heavy data with a 10816 color grid indicator to distinguish normal (green) and abnormal (red) machines at a glance.

  • Reduced visual clutter – Replaced excessive numerical cards with clear, icon-based visuals and summarized status indicators.

  • Anomaly-driven navigation – Enabled direct click-through from any alert to detailed machine or sensor logs, reducing time-to-troubleshoot.

  • Role-based dashboards – Users only see information relevant to their tasks:

    • 👤 Monitoring staff see a high-level alert view

    • 👤 Maintenance teams access issue-specific diagnostics

    • 👤 Analysts can explore deeper performance metrics

Project Task 2

Dashboard Optimization

⚠️ Problem Overview

The existing dashboard was designed as a one-size-fits-all interface, serving three distinct user roles—monitoring personnel, maintenance teams, and analysts—but providing the same level of detail to everyone, leading to unnecessary complexity:

  • Rigid configuration flow – The machine-to-sensor setup process was inflexible, leading to user frustration during configuration.

  • Asynchronous data updates – Delayed sync disrupted real-time monitoring and predictive maintenance.

  • Complex installation – Cumbersome wiring caused increased setup time and a higher risk of error.

🎯 Design Decision: Tailored, Role-Based Dashboards

To address these issues, we partnered with field operators and R&D to redesign the dashboard with a role-specific, visual-first approach that prioritizes clarity and actionability:

  • Visual alert focus – Replaced text-heavy data with a 10816 color grid indicator to distinguish normal (green) and abnormal (red) machines at a glance.

  • Reduced visual clutter – Replaced excessive numerical cards with clear, icon-based visuals and summarized status indicators.

  • Anomaly-driven navigation – Enabled direct click-through from any alert to detailed machine or sensor logs, reducing time-to-troubleshoot.

  • Role-based dashboards – Users only see information relevant to their tasks:

    • 👤 Monitoring staff see a high-level alert view

    • 👤 Maintenance teams access issue-specific diagnostics

    • 👤 Analysts can explore deeper performance metrics

Project Task 3

Settings Design for Storage Control & Secure Notifications

🎯 Design Decision 1: Data Storage Settings
🛠️ User Need

Users require control over data retention to prevent server overload, which can lead to system failures. They also need transparency around what data is deleted and how long it is retained.

💡 Design Solution

Created a flexible and proactive storage management interface that gives users control, while ensuring critical data is preserved for diagnostics:

  • Customizable Data Retention
    Allow users to define retention periods for different data types (Raw Data, FFT, Event logs), optimizing storage based on capacity and relevance.

  • Proactive Notifications
    Send alerts and emails before scheduled deletions, including a confirmation step to prevent accidental data loss.

  • Prioritized Data Preservation
    Automatically delete less critical data first, while retaining essential event metadata (e.g., ISO 10816-3 levels, affected axis) for accurate troubleshooting.

Design Decision 2: Notification & Account Settings
🛠️ User Need

Users need motor status daily via email and system notifications, but system access must comply with cybersecurity protocols. Each user should only see the motors they are responsible for, especially in multi-facility environments.

📬 Design Solution

We designed a secure, role-aware notification system that maintains operational awareness without compromising sensitive data:

  • Automated Daily Notifications
    System and email alerts provide daily motor status updates, ensuring users stay informed with minimal effort.

  • Role-Based Access Control (RBAC)
    Users only see motors and sensors assigned to them. This prevents cross-facility access, aligning with cybersecurity compliance requirements and internal IT policies.

Project Task 3

Settings Design for Storage Control & Secure Notifications

🎯 Design Decision 1: Data Storage Settings
🛠️ User Need

Users require control over data retention to prevent server overload, which can lead to system failures. They also need transparency around what data is deleted and how long it is retained.

💡 Design Solution

Created a flexible and proactive storage management interface that gives users control, while ensuring critical data is preserved for diagnostics:

  • Customizable Data Retention
    Allow users to define retention periods for different data types (Raw Data, FFT, Event logs), optimizing storage based on capacity and relevance.

  • Proactive Notifications
    Send alerts and emails before scheduled deletions, including a confirmation step to prevent accidental data loss.

  • Prioritized Data Preservation
    Automatically delete less critical data first, while retaining essential event metadata (e.g., ISO 10816-3 levels, affected axis) for accurate troubleshooting.

Design Decision 2: Notification & Account Settings
🛠️ User Need

Users need motor status daily via email and system notifications, but system access must comply with cybersecurity protocols. Each user should only see the motors they are responsible for, especially in multi-facility environments.

📬 Design Solution

We designed a secure, role-aware notification system that maintains operational awareness without compromising sensitive data:

  • Automated Daily Notifications
    System and email alerts provide daily motor status updates, ensuring users stay informed with minimal effort.

  • Role-Based Access Control (RBAC)
    Users only see motors and sensors assigned to them. This prevents cross-facility access, aligning with cybersecurity compliance requirements and internal IT policies.

Project Task 3

Settings Design for Storage Control & Secure Notifications

🎯 Design Decision 1: Data Storage Settings
🛠️ User Need

Users require control over data retention to prevent server overload, which can lead to system failures. They also need transparency around what data is deleted and how long it is retained.

💡 Design Solution

Created a flexible and proactive storage management interface that gives users control, while ensuring critical data is preserved for diagnostics:

  • Customizable Data Retention
    Allow users to define retention periods for different data types (Raw Data, FFT, Event logs), optimizing storage based on capacity and relevance.

  • Proactive Notifications
    Send alerts and emails before scheduled deletions, including a confirmation step to prevent accidental data loss.

  • Prioritized Data Preservation
    Automatically delete less critical data first, while retaining essential event metadata (e.g., ISO 10816-3 levels, affected axis) for accurate troubleshooting.

Design Decision 2: Notification & Account Settings
🛠️ User Need

Users need motor status daily via email and system notifications, but system access must comply with cybersecurity protocols. Each user should only see the motors they are responsible for, especially in multi-facility environments.

📬 Design Solution

We designed a secure, role-aware notification system that maintains operational awareness without compromising sensitive data:

  • Automated Daily Notifications
    System and email alerts provide daily motor status updates, ensuring users stay informed with minimal effort.

  • Role-Based Access Control (RBAC)
    Users only see motors and sensors assigned to them. This prevents cross-facility access, aligning with cybersecurity compliance requirements and internal IT policies.

Impact & Outcome

  • Increased diagnostic efficiency – Intuitive UX enhancements allow technicians to detect anomalies faster.

  • Reduced troubleshooting time – Simplified workflows and improved UI elements help users pinpoint issues quickly.

  • Optimized equipment pairing process – Automation and batch operations significantly speed up sensor deployment.

  • Improved system visibility – Real-time monitoring and enhanced data visualization support better maintenance decision-making.


Key Takeaways

  • User-Centered Design Matters – Understanding different roles helped tailor UX solutions for more efficient workflows.

  • Data Visualization is Critical – Clear, real-time dashboards improve anomaly detection and decision-making.

  • Process Automation Drives Efficiency – Automated pairing and issue detection reduce manual intervention and errors.

  • Scalability is Key – A flexible, well-structured UX ensures adaptability to future system upgrades.

Impact & Outcome

  • Increased diagnostic efficiency – Intuitive UX enhancements allow technicians to detect anomalies faster.

  • Reduced troubleshooting time – Simplified workflows and improved UI elements help users pinpoint issues quickly.

  • Optimized equipment pairing process – Automation and batch operations significantly speed up sensor deployment.

  • Improved system visibility – Real-time monitoring and enhanced data visualization support better maintenance decision-making.


Key Takeaways

  • User-Centered Design Matters – Understanding different roles helped tailor UX solutions for more efficient workflows.

  • Data Visualization is Critical – Clear, real-time dashboards improve anomaly detection and decision-making.

  • Process Automation Drives Efficiency – Automated pairing and issue detection reduce manual intervention and errors.

  • Scalability is Key – A flexible, well-structured UX ensures adaptability to future system upgrades.

Impact & Outcome

  • Increased diagnostic efficiency – Intuitive UX enhancements allow technicians to detect anomalies faster.

  • Reduced troubleshooting time – Simplified workflows and improved UI elements help users pinpoint issues quickly.

  • Optimized equipment pairing process – Automation and batch operations significantly speed up sensor deployment.

  • Improved system visibility – Real-time monitoring and enhanced data visualization support better maintenance decision-making.


Key Takeaways

  • User-Centered Design Matters – Understanding different roles helped tailor UX solutions for more efficient workflows.

  • Data Visualization is Critical – Clear, real-time dashboards improve anomaly detection and decision-making.

  • Process Automation Drives Efficiency – Automated pairing and issue detection reduce manual intervention and errors.

  • Scalability is Key – A flexible, well-structured UX ensures adaptability to future system upgrades.

Let's talk

Let's talk

jenchuhsu@gmail.com

jenchuhsu@gmail.com