Case Studies

Reliability case studies: from HALT and HASS to field-failure investigations.

Examples showing how weakness discovery, engineering investigation, root-cause analysis, and improved screening can translate into better field reliability and better decisions.

90% Warranty reduction

HALT to HASS success

A mature product had a 5% warranty return rate and a 72-hour production test process. HALT was used to identify weaknesses and support corrective action and HASS development. Warranty returns were reduced to 0.5%, while production test time was reduced to one hour.

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GM Ignition-switch case
Start with the reliability-engineering analysis

The GM Ignition Switch: When a Fish Scale Revealed What the Reliability Process Missed

Kirk A. Gray — Revised August 2026

This article introduces the GM ignition-switch case from a reliability-engineering perspective. It focuses on the measurable detent-torque weakness, the 2006 spring-and-plunger design change, the failure to assign a new part number, and the direct physical comparisons that eventually exposed differences hidden by configuration records.

Why begin with this article?

The full GM investigation is extensive and addresses corporate culture, decision-making, safety, legal issues, and organizational failures. This shorter analysis provides a focused path into the case for practicing reliability engineers: measure functional margin, investigate the actual hardware, understand unit-to-unit variation, preserve configuration traceability, and do not assume that identical part numbers mean identical physical designs.

The article is commentary and engineering analysis based on the public investigation and related source records. It clearly distinguishes the author's reliability interpretations from findings of the Valukas investigation.

Read Kirk Gray's GM ignition-switch article →

Primary source: Anton R. Valukas, Report to Board of Directors of General Motors Company Regarding Ignition Switch Recalls (2014). The report was prepared by private counsel for GM, so this site links to the NHTSA-hosted copy rather than redistributing it.

Read the underlying Valukas Report on NHTSA →
HALT Published research
Open-access HALT case study

Anomaly Detection of Servomotors Subject to Highly Accelerated Limit Testing

Authors: Tadahiro Shibutani, Shota Tsuboi, and Michael G. Pecht

This 2022 International Journal of Prognostics and Health Management paper reports a HALT study of a programmable robot system with 12 servomotors. The work applies cold, heat, rapid thermal change, vibration, and combined stresses while using machine-learning methods to detect developing anomalies.

Commentary by Kirk A. Gray

This paper provides a useful practical example of what Highly Accelerated Limit Testing (HALT) can reveal when environmental stress is combined with continuous functional and parametric monitoring. Shibutani, Tsuboi, and Pecht subjected a programmable robotic system containing 12 servomotors and associated control electronics to cold, heat, rapid temperature change, vibration, and combined stresses. Rather than simply determining whether the system continued to operate, the researchers monitored electrical behavior and used machine-learning techniques to identify changes that preceded readily observable functional anomalies.

Practicing reliability engineers should pay particular attention to the thermal results. During cold testing, observable servomotor anomalies occurred at −70°C, while during heat testing shaft position began changing above 40°C and continued through the 80°C test limit. These failures were recoverable when temperature returned toward normal conditions—an excellent example of the type of soft, or operational, failure that HALT is intended to expose. Rapid thermal transitions also produced anomalous behavior that was not necessarily apparent through visual inspection alone.

Perhaps the most important lesson is the value of monitoring parameters rather than relying solely on pass/fail functional testing. The researchers found that anomaly scores could begin changing before an operating failure became physically observable. Their Gaussian graphical model detected changes even earlier than the individual k-nearest-neighbor analysis. The results also showed that interactions between the servomotors and their controller mattered—the behavior could not always be understood by considering an individual component in isolation.

For practicing reliability engineers: Do not just ask whether the product is still working. Monitor how it is working as environmental stress is increased. Voltages, timing, current, signal quality, error counts, communications behavior, sensor outputs, and other measurable parameters may begin shifting well before a conventional functional test declares a failure. Those changes can provide valuable clues to shrinking design margin and interactions within the system that deserve investigation.

— Kirk A. Gray

Creative Commons: This article is distributed under the Creative Commons Attribution 3.0 United States License (CC BY 3.0 US), which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.

Source: International Journal of Prognostics and Health Management, 2022. DOI: 10.36001/IJPHM.2022.v13i2.3138

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