No Fault Found (NFF): A Persistent and Costly Problem

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Across defense, aerospace, transportation, energy, manufacturing, and digital infrastructure, organizations continue to struggle with failures that cannot be confirmed or reproduced once equipment is removed from service. These events are commonly labeled No Faults Found (NFFs)and are also widely referred to in the literature as No Trouble Found (NTF), Cannot Duplicate (CND), or Re-Test OK (RTOK). Regardless of terminology, the underlying problem is the same: complex systems exhibit abnormal behavior that disappears under conventional test conditions.

NFFs are rarely the absence of a problem. Instead, NFFs are a symptom of increasing system complexity, tight operating margins, software–hardware interactions, environmental sensitivity, and cross‑subsystem dependencies. Traditional diagnostics, which focus on component‑level thresholds and rule sets, are poorly suited to capture these effects. The result can be costly Service Actions, unnecessary part replacements, extended troubleshooting cycles, and unresolved field issues that quietly erode reliability and operating margins.

TNP addresses and solves NFF problems directly using AI‑enabled Multivariate State Estimation Technique (AI‑MSET), a proven, physics‑consistent approach to modeling and monitoring system behavior as a whole rather than as isolated parts.

Why NFFs Persist as Systems Become More Advanced

Modern systems fail less often in obvious ways. Instead, they drift, interact, and intermittently misbehave under specific combinations of load, timing, software state, and environment. These behaviors:

• Occur only under narrow operating envelopes
• Involve correlated changes across many signals

                     -- Cannot be detected by conventional univariate high/low thresholds

• Are masked when components are removed and bench‑tested in a different environment
• Are influenced by operating context, software updates, and configuration changes

Because traditional diagnostics evaluate univariate telemetry signals independently and rely on predefined high/low alert limits, they frequently miss these conditions. When the system later returns to service, the same behavior reappears, creating chronic NFF loops that drive Operations and Maintenance (O&M) costs, asset/facility downtime, and frustration.

AI‑MSET breaks this cycle by learning what normal looks like across the full operating space and detecting when the system deviates from that learned behavior, even when no single signal violates a high/low limit.

How AI‑MSET Reduces and Prevents NFF

AI‑MSET is a deterministic multivariate advanced pattern recognition technique that continuously models the normal relationships between and among dozens to hundreds of sensor signals (per asset), to hundreds of thousands of telemetry variables (per fleets of assets) in real time. Instead of asking whether a component is “good” or “bad,” it asks a more powerful question: Is the system and its dynamically interacting internal components behaving the way it should, given its current operating conditions and learned correlation patterns trained on past undegraded operations?

Key capabilities include:

• Detection of intermittent and transient dynamic behavior that cannot be reproduced offline in a testing laboratory
• Correlation‑based diagnostics/prognostics that distinguish true faults from contextual anomalies (including spurious sensor anomalies)
• Early warning of abnormal behavior, long before functional failure occurs
  • Conventional univariate high/low parameter alerts are reactive, never proactive
• Reduction/Elimination of false alarms while increasing sensitivity to meaningful changes from incipient degradation modes

By providing objective, behavior‑based empirical evidence, AI‑MSET enables maintenance, engineering, and operations teams to identify root causes rather than cycling through repeated remove‑and‑replace actions. The measurable outcome is fewer unnecessary interventions, shorter time to resolution, and a sustained reduction in NFF‑driven cost.

NFF Across Defense and Commercial Industries

Aerospace and Defense

NFF has long been recognized as a major contributor to aircraft downtime, mission aborts, and logistics churn. Intermittent avionics, sensors, power electronics, and software‑driven interactions often pass test benches while continuing to disrupt operations in service. AI‑MSET provides fleet‑level and platform‑level visibility into abnormal behavior, enabling maintainers to resolve issues that would otherwise persist indefinitely.

Transportation (Rail and Transit)

Rail systems frequently experience NFF‑type events in signaling, switching, communications, and control subsystems. These lead to precautionary slow orders, service disruptions, and repeated investigations without resolution. AI‑MSET helps operators determine whether anomalies are isolated, systemic, or environmental, reducing repeat truck rolls and network‑wide delay propagation.

Electric Power Generation and Transmission

In power systems, unexplained trips, protection actions that test normal, and ambiguous alarms often result in conservative operating decisions. While safe, these decisions reduce availability and economic performance. AI‑MSET improves confidence by explaining what changed in system behavior, allowing faster restoration and fewer unnecessary outages.

Industrial and Discrete Manufacturing

High‑speed, high‑capital manufacturing environments are especially vulnerable to NFF. Lines are stopped, restarted, and stopped again as intermittent issues recur. Each cycle generates scrap, rework, and lost throughput. AI‑MSET enables earlier detection of subtle process deviations, preventing repeat stoppages and stabilizing production.

Semiconductor and Hardware Manufacturing

In semiconductor fabrication, board manufacturing, and system integration, NFF drives yield loss, retest cycles, delayed shipments, and inflated warranty exposure. AI‑MSET monitors process and system behavior across manufacturing and test steps, identifying latent issues before they propagate into the field.

A useful executive framing is that NFF is a silent operational tax—rarely visible in a single line item, but material in aggregate across reliability, availability, and customer trust.

An Underappreciated Root Cause of NFFs: Quantized Signals

One of the most persistent and poorly understood contributors to No Faults Found is the prevalence of quantized signals. In many modern systems, especially digital and computer‑based platforms, sensors and internal telemetry do not vary smoothly. Instead, they report values in discrete bins.  Quantized signals originate from inexpensive analog-to-digital (A/D) chips with low-bit resolution (8-bit A/D chips are very common, even in recent high-tech engineering assets across commercial and defense industries, including modern HPC and AI data centers. Quantized time series signals cause both univariate and multivariate anomaly detection algorithms to perform poorly [high false-alarm and missed-alarm probabilities], and have led to many  costly NFFs in industries. 

Quantized signals are extremely prevalent in enterprise servers and data centers. Power management states, utilization metrics, thermal readings, error counters, throttling indicators, and firmware‑controlled limits are often represented by stepwise or sparsely changing values. From a diagnostic perspective, this presents a fundamental challenge: most anomaly detection methods implicitly assume continuous signals and perform poorly—or fail outright—when applied to quantized data.

The consequence is a major diagnostic blind spot. Abnormal behavior that is real and operationally significant may be dismissed as noise, averaged away, or ignored entirely. Components are removed and tested, found to be functional, and returned to service—only for the same behavior to recur. This dynamic directly feeds chronic NFF cycles, particularly in enterprise computing environments.

TNP addresses this challenge with DeQuantize, an intelligent data preprocessing capability developed through extensive real‑world NFF resolution experience. DeQuantize reconstructs usable behavioral information from highly quantized signals, allowing AI‑MSET to learn normal relationships and detect subtle deviations that would otherwise be obscured. This capability enables reliable anomaly detection and root‑cause isolation in environments dominated by quantized telemetry—an area where most organizations and vendors have little practical expertise.

Focus Area: NFF in Enterprise Servers and Data Centers

Enterprise servers and data centers represent one of the most costly modern manifestations of NFF. These environments combine dense hardware, complex firmware, layered software stacks, aggressive power management, and dynamic workloads. Failures often appear as:

• Intermittent crashes or reboots
• Performance degradation with no clear cause
• Spurious hardware or firmware alerts that clear on reset
• Removed components, system boards, and enterprise servers that test “OK” after replacement

Each NFF event consumes engineering time, disrupts service, and increases operational risk. At scale, NFF issues drive over‑provisioning of very expensive spares, reduced utilization, significantly reduced cluster and data center availability, and elevated operating expenses.

AI‑MSET is uniquely suited (and well proven, see Bibliography of refereed scientific publications) to this environment because it models the interactions among power, thermal, workload, firmware, and hardware behavior in real time. And from sensors already in the assets (AI_MSET for proactive anomaly detection of CPUs, GPUs, system boards, and servers requires no hardware mods anywhere in the data centers). AI-MSET™ identifies when components, subsystems, servers, and clusters are operating outside learned normal patterns—even when no individual sensor appears abnormal or exceeds conventional high/low alert thresholds. This allows operators to:

• Disambiguate true hardware degradation from contextual anomalies
• Avoid unnecessary component replacements
• Identify systemic issues affecting racks, clusters, workloads, and cooling dynamics (both for air-cooled and fluid-cooled IT assets)
• Improves reliability, availability, and serviceability (RAS) for IT assets and avoids very costly warranty losses for the suppliers of the chips, servers, and storage assets from NFF replacements 
• For data center operators, the result is fewer unexplained incidents, fast and accurate root‑cause isolation, and higher confidence in both corrective actions and continued operation.

NFF and the Hidden Cost of Sparing Logistics

Beyond maintenance labor and operational disruption, NFFs imposes a severe and often overlooked burden on global sparing logistics. When a significant fraction of removed components are ultimately classified as No Fault Found, suppliers are forced to carry substantially larger spare‑parts inventories to maintain service levels.

As a simple illustration, a supplier experiencing a 50% NFF rate must stock roughly 50% more spares across regional depots worldwide. These additional spares generate no revenue while consuming capital, storage capacity, transportation resources, and lifecycle management effort. The financial impact compounds across high‑volume platforms, geographically distributed fleets, and long service lives.

For enterprise computing vendors and data center operators, elevated NFF rates translate directly into inflated inventory costs, constrained supply chains, longer mean time to repair (MTTRs), and increased exposure during demand surges or supply disruptions. High NFF levels also distort reliability metrics, masking true failure mechanisms and delaying corrective engineering actions.

By reducing unnecessary removals and repeat replacements, AI‑MSET attacks this problem at its source. Fewer NFF events lead directly to lower spare‑parts demand, leaner global inventories, and more resilient logistics operations. At scale, even modest reductions in NFF rates can unlock substantial working‑capital savings while improving service responsiveness.

Strategic Value of AI‑MSET for NFF Mitigation

As systems continue to grow in complexity, NFF will increase unless diagnostic approaches evolve. AI‑MSET represents that evolution. By focusing on system behavior rather than isolated thresholds, it addresses the root cause of NFF rather than its symptoms.

For organizations deploying AI‑MSET, the benefits are durable and compounding: lower lifecycle cost, higher availability, improved reliability, and restored confidence in system operation. TNP brings decades of proven MSET expertise to this challenge, delivering a practical and scalable path to eliminating chronic No Fault Found across industries.