The Hidden Costs of Conventional FDD Software (And Why AI-based Solutions Are The Way Forward)

Fault Detection and Diagnostics (FDD) software is a critical tool for engineering and maintenance teams tasked with keeping assets and equipment running smoothly. By identifying anomalies and potential failures, FDD systems help prevent downtime, optimize performance, and improve operational efficiency.

However, while conventional FDD solutions have been widely adopted, they come with hidden costs that often go unnoticed until they impact productivity and profitability. In contrast, an AI-first approach to FDD eliminates inefficiencies by leveraging automation, machine learning, and advanced analytics to provide smarter, more reliable diagnostics. AI also enables effective knowledge retention and transfer, helping organizations build capacity and address skills gaps for newcomers into the workforce. 

This article explores the overlooked costs and limitations of conventional FDD practices and why AI-based solutions are the future of fault detection and diagnostics. Let’s start with a brief overview of how existing FDD software solutions work.

Conventional Fault Detection & Diagnostics Software: How it Works

Traditional FDD systems operate on predefined rules and thresholds designed to detect deviations from expected asset behaviour. These systems typically rely on the following core components:

• Static Thresholds: The most basic form of fault detection involves setting fixed limits for equipment parameters such as temperature, pressure, or flow rate. The FDD system triggers an alert if a parameter exceeds its set threshold. This approach is simple to implement but often generates numerous false alarms due to its inability to account for varying operational conditions.
• Predefined Rules: Engineers manually configure rules based on their domain expertise to define specific failure conditions. These rulesets typically involve logical combinations of multiple parameters and thresholds. For example, a rule might specify that if the temperature of a motor exceeds a certain level while the load is above a specific threshold, a fault is suspected.
• Alarm-Based Notifications: When a fault is detected based on predefined rules or threshold breaches, the FDD system generates alarms, prompting engineers or technicians to investigate the issue. These alarms are typically delivered through email, SMS, or integrated dashboards.

Industries such as Facility Management, Energy, and Manufacturing widely use these systems to monitor HVAC systems, electrical infrastructure, industrial machinery, and more. However, the effectiveness of conventional FDD is limited by its reliance on fixed logic that does not adapt to dynamic real-world situations.

Hidden Costs & Limitations of Conventional FDD Software

While traditional FDD software may initially appear to be a reliable solution, a closer examination reveals several hidden costs and inefficiencies that significantly impact asset operations:

1. High False Positives & False Negatives

Conventional FDD generates alarms based on static rules, often leading to false positives (nuisance alarms) or false negatives (missed faults). Engineers spend time investigating alerts that don’t indicate real issues, leading to:

• Alarm Fatigue: Frequent unnecessary alerts desensitize teams, leading them to ignore or overlook genuine issues. For example, an HVAC system in a large office building triggers multiple alarms every day due to minor temperature fluctuations. Over time, the maintenance team may become conditioned to disregard these alarms, potentially missing a critical failure that could lead to significant downtime.
• Delayed Fault Resolution: Genuine failures may go undetected due to rigid rules that fail to capture evolving failure patterns. For instance, a slow degradation in the performance of a pump might not trigger any alarms until the pump completely fails, resulting in unplanned downtime and costly repairs.

2. Manual & Reactive Diagnostics

Typical FDD solutions are often limited to detecting anomalies, lacking automation and sophisticated capabilities for root-cause analysis. This results in consequences like:

• Increased Time-to-Resolution: Engineers must manually analyze data to identify the underlying cause of the issue, a time-consuming and error-prone process.
• Dominance of Reactive Maintenance: Maintenance actions are primarily reactive, addressing issues only after they have already caused problems. This approach results in prolonged equipment downtime and higher operational costs compared to proactive or predictive strategies.

3. Heavy Dependence on Individual Expertise

Conventional FDD relies heavily on the expertise of skilled engineers to interpret alarms, prioritize issues, and adjust rulesets over time.

• Knowledge Gaps: If experienced personnel leave the organization, significant knowledge gaps emerge, making maintenance operations less efficient and more prone to errors.
• Manual Rule-Setting: As asset complexity increases and the volume of data grows, manual rule-setting becomes increasingly unsustainable.

4. Contextual Inaccuracy & Inadaptability

Rule-based systems cannot self-learn and adapt to changing conditions, resulting in contextual inaccuracies and reduced effectiveness.

• Failure to Adapt to Variables: Threshold-based alerts often trigger unnecessary alarms during peak load periods due to the inability to account for seasonal variations or changing operational contexts. For example, an electrical transformer might routinely trigger over-temperature alarms during the summer months, even though its performance is within acceptable limits.
• Inefficient Maintenance Planning: Lack of contextual fault detection results in inefficient maintenance planning, leading to unnecessary maintenance activities or missed opportunities for preventative maintenance.

5. High Maintenance & Configuration Costs

Maintaining the relevance and accuracy of static rule-based FDD requires frequent manual tuning, updates, and customization.

• Investment in Third-Party Consultants: Organizations often need to invest in third-party consultants or internal specialists to continuously update rules and customize the FDD system for different asset types, adding complexity and increasing costs.

How AI-based Solutions Transform the Conventional Approach to FDD 

An AI-first approach to FDD offers a transformative solution to the limitations of conventional systems by leveraging the power of machine learning, advanced analytics, and intelligent process automation.

1. Automated & Adaptive Fault Detection

Unlike conventional systems, AI-powered FDD software learns from historical and real-time asset performance data to dynamically detect anomalies without being limited by static rules.

• Reduced False Alarms: This approach significantly reduces false positives and false negatives, ensuring more actionable insights and minimizing wasted effort.
• Real-Time Adaptation: The system adapts to operational changes in real time, continuously improving detection accuracy and providing a more reliable representation of asset health.

2. Root-Cause Analysis & Predictive Insights

AI-driven FDD goes beyond simple fault detection by identifying patterns and trends in asset behavior, providing probable root causes of failures, and enabling predictive maintenance strategies.

• Proactive Maintenance: By identifying potential issues before they escalate, maintenance teams can proactively address problems, minimizing downtime and reducing repair costs.

3. AI-Augmented Engineering Workflows

AI-powered FDD acts as a decision-support system for engineers, offering:

• Automated fault diagnostics, reducing manual troubleshooting.
• Prioritized issue resolution, ensuring high-impact failures are addressed first.
• Continuous improvement, refining detection accuracy with every fault encountered.

4. Continuous Learning & Adaptation

AI analyzes vast amounts of operational data and learns from different workflows, user interactions, and new scenarios to continuously improve its performance over time.

• Dynamic Adjustments: It dynamically adjusts to seasonal variations, changing asset conditions, and evolving operational patterns, ensuring that the FDD system remains accurate and effective.
• Engineers can trust AI-powered recommendations rather than relying solely on static rules.

5. Cost Savings & Efficiency Gains

The AI-first approach delivers significant cost savings and efficiency gains through:

• Reduced Labor Costs: Fewer false alarms reduce wasted labour hours spent investigating non-existent issues.
• Minimized Downtime: Faster diagnostics minimize equipment downtime, resulting in increased productivity and reduced revenue losses.
• Scalability: AI-driven automation reduces dependence on manual expertise, ensuring scalability and enabling organizations to manage a larger number of assets with fewer resources. Fewer false alarms reduce wasted person hours.

Embracing AI-driven FDD: Why Now?

The increasing complexity of modern assets demands a smarter, more scalable approach to fault detection and diagnostics. Several key factors are driving the adoption of AI-first FDD:

• Industry Trends: A growing emphasis on predictive maintenance is pushing organizations to seek more advanced FDD solutions.
• Proactive Asset Management: AI-powered FDD enables organizations to transition from reactive to proactive asset management, minimizing downtime and maximizing asset lifespan.
• Strategic Orientation: Engineering and maintenance teams can shift from spending hours investigating alarms to taking strategic actions based on intelligent insights, improving overall operational efficiency.

How to Transition to an AI-first FDD Approach?

1. Assess Your Current FDD Solution

• What challenges are you facing with conventional FDD?
• Are you experiencing high false alarms, slow diagnostics, or manual inefficiencies?

2. Define Key Objectives

• Do you want to reduce false alarms or prioritize critical issues?
• Are you aiming to automate fault diagnosis and improve response times?

3. Look for AI-First Capabilities

• Ensure the system includes machine learning-based anomaly detection.
• Check for automated root-cause analysis and predictive maintenance insights.
• Prioritize solutions that integrate seamlessly with existing Building Management Systems (BMS) or IoT platforms.

4. Test & Scale AI-Driven Solutions

• Start with a pilot focusing on critical assets.
• Evaluate the accuracy, efficiency gains, and cost savings.
• Scale the AI-first FDD approach across multiple facilities and asset types.

Conclusion: A New Approach Awaits

The limitations of conventional FDD—false alarms, manual diagnostics, high maintenance costs, and limited scalability—are no longer sustainable in today’s dynamic and demanding operational environment. AI-powered FDD eliminates these inefficiencies by providing smarter, automated, and adaptive diagnostics that enhance asset reliability, reduce operational costs, and enable proactive maintenance strategies. 

Are you ready to experience the next generation of FDD? Book a demo with our product expert today to see how AI-powered fault detection and diagnostics can transform your operations, improve your bottom line, and unlock new levels of operational excellence.