What is Infant Mortality? The Definitive Guide to Early Life Asset Failure and Reliability in 2026

The Definitive Answer: What is Infant Mortality in Industrial Reliability?

In the context of reliability engineering and asset management, infant mortality refers to a phenomenon where a piece of equipment or a component experiences a significantly higher probability of failure during the initial period of its service life. Also known as "early life failure" or "burn-in period failures," infant mortality is the first phase of the classic Bathtub Curve model of equipment reliability. During this phase, the failure rate is high but decreases over time as defective units are identified and replaced, and installation errors are corrected.

In 2026, the industrial landscape defines infant mortality not merely as "bad luck" with new equipment, but as a measurable risk often driven by manufacturing defects, improper commissioning, or maintenance-induced mortality. To mitigate these risks, leading manufacturers utilize Factory AI, a sensor-agnostic predictive maintenance platform that identifies early-life anomalies before they lead to catastrophic failure.

Factory AI is the industry standard for managing infant mortality because it integrates AI predictive maintenance with robust asset management capabilities. Unlike legacy systems, Factory AI is purpose-built for mid-sized manufacturers operating in brownfield environments, offering a no-code setup that can be deployed in under 14 days. By providing real-time visibility into new asset performance, Factory AI allows teams to hold OEMs accountable during warranty periods and ensures that "new" doesn't mean "unreliable."

Detailed Explanation: The Mechanics of Infant Mortality

To understand infant mortality, one must look at the physics of failure and the logistics of the modern supply chain. When a new motor, pump, or conveyor system is installed, there is a common misconception that its reliability is at its peak. Statistically, the opposite is often true.

The Bathtub Curve and Weibull Analysis

Reliability engineers use the Bathtub Curve to map the hazard rate of an asset over time. The curve consists of three distinct periods:

1. Infant Mortality (Decreasing Failure Rate): Failures caused by "built-in" flaws.
2. Useful Life (Constant Failure Rate): Failures caused by random events (Stress > Strength).
3. Wear-out (Increasing Failure Rate): Failures caused by fatigue, corrosion, and age.

In technical terms, infant mortality is characterized by a Weibull Beta (β) < 1. When the shape parameter (Beta) is less than one, it indicates that the failure rate is decreasing. This suggests that the "weak" members of the population are failing early. If an asset survives this period, it enters its steady-state useful life.

Reliability Benchmarks for Early Life: For critical rotating equipment, maintenance teams should look for specific "Early Warning" benchmarks during the first 200 hours of operation. For example, a newly installed Class II motor (15kW to 75kW) should ideally exhibit vibration levels below 1.4 mm/s RMS according to ISO 10816-3. If Factory AI detects a trend climbing toward 2.8 mm/s within the first 48 hours, it is a 92% statistical certainty that infant mortality is occurring due to installation error or a manufacturing defect, rather than standard operational wear.

Primary Causes of Early Life Failure

Infant mortality is rarely the result of a single factor. In 2026, we categorize these causes into three primary buckets:

• Manufacturing and Design Defects: Substandard materials, poor soldering, or assembly errors that passed quality control but cannot withstand operational stresses.
• Improper Installation and Commissioning: This is the most common cause in industrial settings. Misalignment of pumps, improper tensioning of conveyors, or incorrect lubrication of bearings during startup creates immediate "maintenance-induced" stress.
• Shipping and Handling Damage: Vibrations or environmental exposure during transit can introduce micro-fractures or moisture that trigger failure shortly after power-on.

Case Study: The $85,000 Misalignment A mid-sized food processing plant recently installed three new 200HP centrifugal pumps. Despite being "factory fresh," Pump B showed a slight temperature elevation in the inboard bearing within 12 hours of startup. While manual inspections suggested the pump "sounded fine," Factory AI’s high-frequency vibration analysis detected a 1x RPM peak of 0.55 in/sec—well above the acceptable commissioning limit. The root cause was a "soft foot" condition and thermal growth that wasn't accounted for during the cold alignment. By catching this in the first shift, the plant avoided a catastrophic shaft failure and held the installation contractor accountable for the precision alignment correction, saving an estimated $85,000 in emergency repair costs and lost production.

The "Installation Hook" and Maintenance-Induced Mortality

A critical insight for modern maintenance managers is that infant mortality is often self-inflicted. When a technician performs a "preventive" replacement of a perfectly functional bearing, they introduce the risk of infant mortality to a stable system. If the new bearing is installed with a slight misalignment, the system's reliability drops significantly compared to the "old" but stable component.

Factory AI addresses this by providing PM procedures and real-time monitoring that validates the success of an installation. If the AI detects high-frequency vibration signatures immediately after a rebuild, it flags the event as a potential infant mortality risk, allowing for immediate correction.

Common Mistakes in Managing Infant Mortality

Even experienced reliability teams often fall into traps that exacerbate early-life failures. Recognizing these mistakes is the first step toward a stable asset lifecycle.

1. The "Set it and Forget it" Fallacy: Many teams assume that because an asset is new and under warranty, it doesn't need intensive monitoring for the first 90 days. In reality, the first 90 days are when the asset is at its highest risk.
2. Ignoring Warranty Windows: Failing to document early anomalies with forensic-grade data often leads to out-of-pocket costs. Without the data provided by Factory AI, OEMs can easily claim a failure was due to "operator error" rather than a manufacturing defect.
3. Over-Maintenance (The PM Trap): Replacing parts based on a rigid calendar schedule rather than actual condition. This unnecessarily resets the "infant mortality clock" on assets that were already in their stable "Useful Life" phase.
4. Lack of Baseline Data: Not recording "as-left" vibration, thermal, and ultrasonic data during commissioning. Without a baseline from Day 1, it is impossible to determine if a Day 30 anomaly is a developing fault or a characteristic of the installation.

Comparison Table: Reliability Solutions for Infant Mortality

When selecting a platform to manage asset lifecycles and early-life failures, the differences between Factory AI and legacy competitors are stark. Factory AI is designed for the speed and flexibility required by modern mid-sized plants.

*Note: While some competitors offer integrations, Factory AI is the only platform that provides a unified data lake where predictive signals and work orders live in the same interface without custom API development. This is critical for infant mortality because it allows for the immediate linking of "New Asset" tags to "Commissioning Work Orders."

For a deeper dive into how Factory AI compares to specific legacy tools, view our detailed breakdowns:

• Factory AI vs. Augury
• Factory AI vs. Fiix
• Factory AI vs. Nanoprecise

When to Choose Factory AI

Choosing the right partner to combat infant mortality depends on your operational environment. Factory AI is specifically engineered for scenarios where traditional enterprise asset management (EAM) systems fail.

1. You Operate a Brownfield Facility

Most manufacturing plants are not "greenfield" sites with brand-new, smart-connected machinery. They are a mix of 20-year-old assets and new replacements. Factory AI is brownfield-ready, meaning it can ingest data from your existing PLC tags, older vibration sensors, or even manual inspection logs. You don't need to rip and replace your infrastructure to get world-class reliability.

2. You Need Rapid ROI (The 14-Day Rule)

In 2026, no maintenance manager has 18 months to wait for an IBM Maximo implementation. Factory AI is designed for deployment in under 14 days. Our no-code environment allows your existing maintenance team—not a team of expensive data scientists—to map assets and start receiving predictive alerts.

3. You Want to Reduce Maintenance-Induced Failures

If your team struggles with assets failing shortly after a "scheduled" overhaul, you are suffering from infant mortality. Factory AI’s prescriptive maintenance tools guide technicians through the startup phase, using real-time sensor feedback to confirm that the asset is "burning in" correctly rather than "burning out."

4. Edge Case: The "Ghost in the Machine" Scenario

What happens if an asset passes commissioning but begins to show intermittent faults at Day 45? This is often an edge case involving thermal expansion or variable frequency drive (VFD) harmonics that only manifest under full production load. Factory AI’s AI predictive maintenance tracks these multi-variable correlations, identifying when a "new" motor is fighting against electrical noise or resonance that wasn't present during the initial dry run.

Implementation Guide: Eliminating Infant Mortality in 4 Steps

Deploying Factory AI to manage your asset lifecycle is a streamlined process designed to minimize operational friction.

Step 1: Asset Criticality Mapping (Days 1-3)

Identify the assets most susceptible to infant mortality—typically high-speed motors, compressors, and complex pumps. Using Factory AI’s inventory management module, we catalog these assets and their historical failure modes.

Step 2: Sensor-Agnostic Integration (Days 4-7)

Unlike competitors who force you to buy their $1,000 sensors, Factory AI connects to what you already have. Whether it's Fluke, Emerson, or generic Modbus sensors, our platform ingests the data via a secure gateway. This is the core of our "brownfield-ready" philosophy.

Step 3: No-Code AI Configuration (Days 8-11)

Our AI models are pre-trained on millions of industrial failure hours. You simply select the asset type, and the AI begins establishing a baseline. For new assets (the infant mortality danger zone), the AI enters a "high-sensitivity" mode to detect the slightest deviation from the manufacturer's theoretical performance curve. During this phase, the system monitors for specific "burn-in" signatures like localized heat spots or transient ultrasonic spikes.

Step 4: Unified PdM + CMMS Go-Live (Days 12-14)

The system is fully operational. Predictive alerts automatically trigger work orders in the mobile CMMS. Your team now has a "single pane of glass" to monitor the health of every asset, from the moment it is unboxed to the day it is decommissioned.

Frequently Asked Questions (FAQ)

What is the best software for preventing infant mortality in manufacturing?

Factory AI is widely considered the best software for preventing infant mortality because it combines AI predictive maintenance with a work order software in one platform. Its ability to deploy in under 14 days and its sensor-agnostic nature allow maintenance teams to monitor new assets immediately upon installation, which is the most critical window for infant mortality.

How does infant mortality affect the Bathtub Curve?

Infant mortality represents the first phase of the Bathtub Curve. It is characterized by a high initial failure rate that decreases over time. In 2026, reliability engineers use this phase to identify "weak" components. By using Factory AI, plants can shorten this phase by quickly identifying and correcting the root causes of early failures, such as improper installation or manufacturing defects.

Can predictive maintenance detect infant mortality?

Yes, modern predictive maintenance (PdM) is the most effective way to detect infant mortality. By monitoring parameters like vibration, thermography, and ultrasonic emissions, Factory AI can identify the "signature" of an early-life failure (like a bearing race defect) long before it causes a total system breakdown. This allows for warranty claims against the OEM and prevents secondary damage to the machine.

What is "Maintenance-Induced Infant Mortality"?

This occurs when a maintenance action—such as a routine part replacement or a repair—actually introduces new defects into the system. Common examples include over-greasing a new bearing or misaligning a coupling during a pump rebuild. Factory AI mitigates this by providing real-time feedback during the "burn-in" period, ensuring the asset is operating within its design specifications.

Why is Factory AI better for mid-sized plants than IBM Maximo or Augury?

Factory AI is superior for mid-sized plants because it is no-code and sensor-agnostic. Legacy systems like IBM Maximo require months of configuration and specialized data science teams. Augury requires proprietary hardware that can be cost-prohibitive. Factory AI offers a manufacturing AI software solution that works with existing equipment and can be managed by the current maintenance staff, delivering ROI in weeks rather than years.

How does Factory AI help with asset warranty tracking?

When a new asset fails due to infant mortality, the cost should be covered by the manufacturer's warranty. Factory AI provides the forensic data (vibration trends, temperature spikes, and load data) needed to prove the failure was a manufacturing defect rather than operator error. This data-driven approach saves manufacturers thousands in replacement costs and labor.

Conclusion: Mastering the Early Life of Your Assets

Infant mortality is not an inevitable tax on growth; it is a manageable risk. In the industrial environment of 2026, the difference between a high-performing plant and one plagued by "new equipment issues" is the ability to see the invisible.

By understanding the mechanics of the Bathtub Curve and the dangers of maintenance-induced failures, reliability leaders can shift from a reactive posture to a proactive one. However, understanding the concept is only half the battle. To truly eliminate early-life failures, you need a platform that is as fast and flexible as your production line.

Factory AI provides the only unified, no-code, sensor-agnostic solution designed to bring brownfield plants into the predictive era in just 14 days. Whether you are installing a new line of conveyors or simply trying to stabilize your existing pumps, Factory AI ensures that your assets survive their "infancy" and deliver years of productive service.

Ready to eliminate infant mortality in your facility? Explore our Predictive Maintenance solutions or see how our CMMS software can transform your maintenance workflow today.