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Predictive Maintenance: How AI is Reducing Unplanned Downtime in Manufacturing
Articles- Jul 28,25
Predictive maintenance is no longer a luxury—it’s a necessity. By integrating AI with IoT, digital twins, and edge computing, manufacturers can proactively address equipment issues, maximise uptime, and create smarter, more resilient production systems, says Pandarinath Siddineni, Domain Head – Industrial Design, Systems & Software, Tata Elxsi
In today’s fast-paced manufacturing world, every second of downtime can translate into lost revenue, delayed delivery timelines, and diminished customer trust. Traditional maintenance strategies—whether reactive or time-based—often fall short of meeting the demands of modern production systems. In such a context, predictive maintenance, a transformative approach powered by artificial intelligence (AI), helps manufacturers reduce unplanned downtime, optimize operations, and extend equipment life.
Evolution of predictive maintenance
The journey of maintenance in manufacturing has witnessed a steady evolution. Initially, maintenance was largely reactive; equipment was fixed only after it broke down. While straightforward, this method proved costly, causing production halts and safety concerns.
The next phase was preventive maintenance, which involved servicing machines at regular intervals based on average usage metrics. This approach brought some predictability to operations but wasn’t always efficient. Maintenance often occurred too early, wasting resources, or too late, risking failure.
With the emergence of sensor technologies and the Industrial Internet of Things (IIoT), manufacturers began exploring condition-based maintenance. This involved monitoring parameters like temperature, vibration, or pressure to assess machine health in real time. While an improvement, it lacked the predictive power of today’s AI-driven systems.
Now, predictive maintenance represents the next frontier. Instead of merely reacting to faults or servicing based on fixed schedules, AI algorithms analyze historical and real-time data to forecast when a machine is likely to fail—allowing for timely, targeted interventions. This paradigm shift enables organizations to achieve both operational excellence and cost efficiency.
AI’s role in predictive maintenance
AI acts as the brain behind predictive maintenance. Machine learning models digest massive volumes of data—from vibration signals and audio cues to current and temperature fluctuations—and identify patterns that precede machine failures.
One of the greatest advantages of AI in this context is its ability to handle complex, multivariate inputs. For instance, a machine might appear to operate normally under a single condition but exhibit subtle anomalies when analyzed across multiple parameters. AI identifies these nuanced correlations and predicts failures long before they happen.
Edge computing and digital twins further amplify this capability. Data from shop floor equipment is either processed on-site (at the edge) for real-time decision-making or integrated into digital twins—virtual replicas of physical assets—to simulate performance and detect anomalies. In fact, audio analytics, an unconventional yet highly effective method, is gaining traction in scenarios where visual or vibration data is insufficient. In one use case, a microphone placed near a washing machine successfully captured audio signatures to predict impending component failures.
Moreover, AI's decision-making isn't confined to centralised systems. Increasingly, autonomous AI agents are being deployed directly on the shop floor to monitor equipment health in real-time and trigger maintenance actions without human intervention.
Industry impact of predictive maintenance
The impact of predictive maintenance is being felt across a variety of industries—from automotive and aerospace to FMCG and energy.
Take the example of tool wear detection in CNC machining. Cutting tools have a finite lifespan, but the exact wear depends on variables like material properties, feed rate, and temperature. Using vibration and current data, AI models can estimate when a tool is nearing the end of its usable life and schedule a replacement just in time—avoiding both premature changes and sudden failures.
In another case, a digital twin implementation for a coffee drying process helped maintain consistent granule sizes by adjusting machine settings in real-time based on predictive models. By continuously monitoring parameters like humidity, temperature, and feed rate, the system not only ensured consistent quality but also minimized energy use and maintenance needs.
Even more innovative, AI-driven acoustic analysis was deployed in a commercial laundry setting to monitor washing machines. By analyzing changes in sound patterns, the system could detect early signs of mechanical issues—such as worn bearings—and trigger maintenance alerts before the machine fails.
Across sectors, predictive maintenance has delivered measurable returns: improved uptime, better product quality, reduced maintenance costs, and optimised use of manpower. These gains are not just theoretical—real-world implementations consistently show tangible ROI through reduced downtime and enhanced throughout.
The future of predictive maintenance with AI
As technologies mature, the future of predictive maintenance lies in deeper integration of AI with edge computing, digital twins, and autonomous systems.
Edge computing is already making it possible to process data right at the source—critical in environments where milliseconds matter, such as high-speed production lines or autonomous robots. Real-time decision-making on the edge eliminates latency and enhances responsiveness.
Meanwhile, digital twins are evolving beyond static replicas. Today’s advanced digital twins can simulate future performance based on current operating conditions, helping manufacturers foresee issues, test solutions virtually, and implement changes with confidence.
Agentic AI—the next step in autonomous systems—holds the potential to transform predictive maintenance into a self-healing system. Though still emerging, the goal is clear: AI agents that not only detect faults but also plan, schedule, and even execute maintenance tasks autonomously. In highly regulated or mission-critical sectors like semiconductors or aerospace, these systems could soon become the standard.
There’s also a strong focus on sustainability. By extending equipment lifespan, reducing energy waste from inefficient machines, and avoiding unnecessary replacements, predictive maintenance supports manufacturers’ decarbonization goals. In a world increasingly focused on ESG performance, this operational edge can translate into reputational and regulatory advantages.
The Indian industrial landscape
India is on the cusp of a manufacturing transformation. With the country positioning itself as a global manufacturing hub, predictive maintenance has emerged as a critical enabler of quality, efficiency, and competitiveness.
While large enterprises in sectors like automotive, railways, and energy have begun implementing AI-powered maintenance strategies, adoption among small and medium-sized manufacturers is still evolving. Many are aware of the benefits but concerns around upfront investment and integration with legacy systems act as barriers.
Encouragingly, technologies like industrial data fabrics—middleware platforms that unify shop floor, ERP, and engineering data—are helping bridge the gap between legacy systems and AI-driven insights. As awareness grows and costs fall, wider adoption across India’s industrial base is inevitable.
Predictive maintenance is no longer a luxury—it’s a necessity. In a world where product margins are thin, competition is global, and customer expectations are high, unplanned downtime is a cost most manufacturers simply can’t afford. By integrating AI with IoT, digital twins, and edge computing, manufacturers can proactively address equipment issues, maximize uptime, and create smarter, more resilient production systems. As India accelerates its digital transformation journey, embracing predictive maintenance will be key to unlocking operational excellence and global competitiveness.
The road ahead isn’t just about maintaining machines—it’s about future-proofing manufacturing itself.
About the author:
Pandarinath Siddineni is the Domain Head – Industrial Design, Systems & Software at Tata Elxsi. He is an established leader with over 27 years of experience in the digital transformation space. As the domain head of systems & software at Tata Elxsi, he works on Industry 4.0 with IIoT, Digital Twin, AI, and MES layer.
Predictive maintenance
Artificial intelligence
AI
Unplanned downtime
Industrial IoT
Digital twins
Edge computing
Manufacturing efficiency
Operational excellence
Pandarinath Siddineni
Industrial Design
Tata Elxsi
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