DAQ as an Instrument for Reliability Verification

Keywords: Data Acquisition (DAQ), Reliability Engineering, Accelerated Life Testing (ALT), Environmental Stress Screening (ESS)

Reliability is a fundamental concept in the fields of engineering, manufacturing, information systems, and quality management. It is defined as the probability that a product, system, or process performs its intended function successfully under specified conditions for a designated period of time. It encompasses not only hardware durability, but also software stability, human factors engineering, and lifecycle management. Reliability serves as a cornerstone for achieving high availability and maintainability/serviceability in modern industries, and is commonly addressed within the frameworks of RAMS (Reliability, Availability, Maintainability, and Safety) or RAS (Reliability, Availability, and Serviceability).
Refer to: Reliability, Availability and Serviceability – Wikipedia, the Free Encyclopedia

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Fundamental Definition and Mathematical Models of Reliability
Refer to: Arthur Tseng, Supply Chain and Enterprise Resource Management (ERM) Dictionary

Reliability is typically expressed as a probability function: R (t) = P (T > t), where T is the time to failure, and t is the observation time. It represents the probability that a system operates without failure up to time t. For example, if an electronic component has a 95% chance of not failing within five years under normal conditions, this can be written as: R(5 years) = 95%.

Key mathematical models include the Exponential Distribution. Under the assumption of a constant failure rate λ, the reliability function is given by R (t) =e^ {-λt}, where the Mean Time to Failure (MTTF) is 1/λ. In addition, the Weibull Distribution is commonly used to describe failure patterns at different stages. Its shape parameter β determines the failure characteristics: β < 1 decreasing failure rate over time; β= 1 constant failure rate; β> 1 increasing failure rate over time.

The Bathtub Curve is a classic graph in reliability engineering that divides a product’s lifecycle into three stages: the early failure period (a high failure rate requiring the screening of defects); random failure period (constant failure rate, typically observed during the product’s stable operation phase); wear-out failure period (an increasing failure rate due to material fatigue). For the early failure period, the industry commonly implements Environmental Stress Screening (ESS) or Burn-in testing, where environmental variations such as temperature cycling are applied in constant-temperature and constant-humidity chambers or high-temperature ovens to identify and eliminate inherently weak components early. For the wear-out failure period, Accelerated Life Testing (ALT) is used to estimate the long-term lifespan limits of products.

Referenced from: https://zh.wikipedia.org/zh-tw/Bathtub Curve – Wikipedia

The Role of DAQ in Reliability Verification
During ESS screening or ALT testing, Data Acquisition (DAQ) systems play a critical role. By accurately and continuously acquiring data such as temperature, voltage, and stress, DAQ systems ensure that the product’s condition under various harsh conditions is fully recorded. In high-standard fields such as aerospace, unmanned aerial vehicles (UAVs), and electronic component certification, DAQ systems often integrate multiplexers and precision digital multimeters (DMMs). For example, the GW Instek DAQ-9600, paired with a 40-channel single-ended multiplexer (e.g., DAQ-903), can support up to 120 measurement points under full load, simultaneously measuring voltage, resistance, and temperature. This enables engineers to precisely analyze product performance across the stages of the Bathtub Curve.

To prevent measurement errors from affecting verification results, the DAQ itself must possess very high hardware specifications. Whether recording multi-point thermal vacuum data during satellite integration tests or analyzing sensor drift in wind tunnel experiments, a DAQ with 6^½-digit high resolution and 0.0035% basic DC voltage accuracy can provide stable and precise data, serving as a cornerstone for improving product quality.

Design Principles to Ensure DAQ Reliability
High-quality DAQ systems employ modular designs and high/low-voltage isolation at the hardware level (for example, differential multiplexer modules supporting up to 300 V isolation) to prevent Common Cause Failures (CCF). The internal low-noise programmable gain instrumentation amplifiers (PGIA) and precision analog-to-digital converters (ADC) must feature minimal total harmonic distortion (THD) and maximized common-mode rejection ratio (CMRR) to ensure signal purity.

More importantly, for reliability verification of systems requiring extremely long test durations—such as manned spacecraft or automotive electronics, which may exceed six months—the DAQ-9600 features a built-in monitoring function for relay switching counts. This design allows users to clearly track whether the relays are approaching their physical lifespan limits, enabling timely maintenance or replacement. By doing so, it prevents interruptions in testing due to internal relay failure, avoids delays in verification schedules, and mitigates the risk of critical test data being lost, which could otherwise result in verification failure.

GW Instek has long been dedicated to the test and measurement field. Its products undergo rigorous reliability and ISO certification processes and are fully compatible with a wide range of temperature sensors, strain gauges, and load cells. This ensures that system-level DAQ solutions consistently deliver the most reliable data during long-term reliability testing.

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Diana

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E-mail: diana@goodwill.com.tw