2026 HBK Durability and Reliability Technology Days

HBK is an established supplier of Self Noise Monitoring Systems (SNMS), also referred to as Hull Vibration Monitoring Equipment (HVME), providing integrated solutions that support navies in the measurement, assessment, and management of Underwater Radiated Noise (URN) for both submarine and surface combatant platforms.

This presentation provides an overview of the physical principles and environmental factors governing underwater acoustics and examines their significance within modern naval operations and mission profiles. Drawing on extensive expertise in acoustic signature management, particular emphasis will be placed on the role of URN monitoring and control in enhancing platform stealth, survivability, and overall operational effectiveness.

• For submarines, maintaining low radiated noise is critical to minimizing the probability of detection.
• For Anti-Submarine Warfare (ASW)-capable surface combatants, effective self-noise management is essential to maximize sonar performance and enable the detection, classification, and tracking of underwater contacts.

The presentation will conclude by describing HBK’s range of configurable COTS vessel-embedded SNMS solutions, ranging from fully integrated turnkey systems, operating either as standalone installations or interfaced with the ship's Platform Management System (PMS), to flexible monitoring and analysis solutions based on scalable data acquisition platforms to support acoustic signature management throughout the platform lifecycle. Drawing upon experience gained across numerous naval programmes, including publicly disclosed platforms such as the Astute-class submarine, Type 26 City-class frigate, and Hunter-class frigate, these solutions deliver robust, scalable, and future-proof acoustic monitoring capabilities for modern naval fleets. 

Wireless sensor networks (WSNs) consist of independent sensing devices that communicate wirelessly with a central base station, enabling rapid deployment of scalable monitoring systems while avoiding many limitations of traditional wired installations. When combined with onboard implementation of the Rainflow Counting Algorithm, wireless nodes can perform fatigue-cycle analysis directly at the measurement source, eliminating the need to transmit large volumes of raw sensor data for centralized processing.

Wireless networks are particularly well suited for Structural Health Monitoring (SHM) because they reduce installation complexity, lower material and labor costs, and simplify deployment on large or difficult-to-access structures. They also avoid common wired-system issues such as voltage drop, electromagnetic interference, and cable failures caused by vibration or structural movement.

The Rainflow Counting Algorithm is a widely accepted fatigue-analysis method that identifies and counts stress-reversal cycles within complex loading histories. By converting raw strain or stress measurements into discrete fatigue events, Rainflow processing extracts information relevant to fatigue damage accumulation while filtering out noise and non-damaging events.

Combining wireless sensing with onboard Rainflow analysis creates a more efficient and scalable SHM architecture. Processing data at the edge reduces communication errors, network traffic, storage requirements, and post-processing effort by transmitting only meaningful fatigue-cycle information. The result is faster structural integrity assessment and more efficient long-term monitoring, providing fatigue-cycle counts, cumulative damage estimates, and remaining useful life indicators with minimal communication and computational overhead.

In real-world applications structural components often experience changing loading and operating conditions that cause complex stress states where the principal stress directions are unknown and change through time. In these conditions traditional single gauge strain measurements are unable to capture the complex stress state and limit the potential post-measurement analyses. This presentation demonstrates measurements with strain gauge rosettes, shows how to calculate principal strain and principal stress during measurement, gives you the knowledge to decide whether single or rosette strain gauges are appropriate for your measurement applications, and concludes by showing how to use strain gauge rosettes to uncover hidden stresses.

Join us to learn how strain gauge rosettes provide a powerful solution for:

1. Efficiency and Precision – the advantages of using multiple strain gages on a common carrier reduce installation work and ensure high accuracy in the location and orientation of measuring grids.
2. Comprehensive Stress Analysis – the formulas and methods required to calculate principal strains and principal stresses, and principal direction. These are critical for analyzing biaxial stress conditions where principal directions are unknown, to accurately assess material stresses where forces act in various directions.
3. Uncovering Hidden Stresses – the critical role of rosettes in investigating residual stresses within structural parts using specialized techniques like the drill-hole method and the ring-core method. These methods provide insights into the stresses present without external loading.

Strain gauge rosettes are widely used to measure complex stress and strain states, providing the foundation for many durability and fatigue assessments. However, understanding how these measurements relate to fatigue damage, crack growth, and fatigue life prediction can be challenging.

This presentation provides a practical introduction to multiaxial and biaxial stress analysis and demonstrates how rosette measurements can be transformed into meaningful fatigue insights. Key concepts including stress and strain tensors, principal stresses, Mohr's Circle, and common measures of biaxiality are introduced using intuitive engineering examples.

The session then examines how biaxial loading influences fatigue behaviour, why many fatigue failures remain predominantly uniaxial, and under what conditions more advanced multiaxial fatigue methods become necessary. Fatigue assessment techniques ranging from signed absolute maximum principal stress methods through signed Von Mises approaches to critical plane analysis are compared, highlighting their strengths and limitations for both simulation-based and measurement-based durability studies.

Finally, attendees will see how an nCode glyph enhancement simplifies automatically deriving critical plane stress and strain histories from strain gauge rosette measurements, enabling more accurate fatigue assessments for complex loading conditions..

The development and validation of products require physical/virtual vibration testing to ensure they are durable. Often these vibration power spectral density (PSD) specifications come from existing aerospace, defense, and automotive standards. However, due to rapid product enhancements and evolving product usage needs, generating suitable PSD profiles by analyzing measured service history and sensor data recorded at proving grounds or flight test centers becomes relevant to a robust product development process. A common challenge when creating these PSDs from sensor data is that a large number of frequency breakpoints are generated. These complex PSDs need to be simplified from 1000s of points to 10s of points due to a variety of reasons such as shaker controller limitations, manual input required for test rigs programs, simplified PSDs needed for input to finite element (FE) solvers, or to compare against existing vibration standards that may have coarser frequency breakpoint distributions.

The challenge is that PSD breakpoint simplification requires more than just peak/valley extraction as the resultant simplified PSD needs to capture the overall shape and behavior of the original PSD to properly characterize its vibration energy precisely and is limited by the number of allowable breakpoints. Standard peak/valley extraction algorithms can fail to capture the overall PSD shape and often necessitates a user to manually pick the breakpoints one at a time, which can be tedious, time consuming, and error prone. Thus, a method of automated breakpoint identification was developed for random PSD vibration curves to precisely capture the shape of the PSD curve while minimizing the error associated with the simplification process. This method allows an automated fit to be performed on the original PSD using user-controlled number of breakpoints and provides the user flexibility to select critical frequencies; hence, minimizing the manual effort required and accelerating test specification development times. 

The FIDES 2022 methodology represents a major evolution in the assessment of electronic equipment reliability. By combining technological constraints, physics-of-failure principles, development processes, and industrial feedback, it enables reliability predictions that are more representative of the actual performance of products in different environments.

The application of FIDES 2022 provides several business benefits, including improved credibility of reliability analyses, optimization of design choices, better control of technical risks, and potential reduction of costs associated with in-service failures. It also promotes a harmonized approach between development teams, RAM experts, and customers through a methodology recognized in demanding industries such as railways, aerospace, space, and defense.

However, the adoption of FIDES 2022 also has an impact on the RAM methodology. It requires the assessment of development processes, qualification of input data, enhancement of team competencies, and adaptation of analysis tools. These changes must be anticipated to ensure the methodology is applied correctly.

This presentation aims to review the principles of the FIDES 2022 methodology, the key developments compared with previous versions, and the main technical and organizational impacts associated with its deployment.

Reliability engineers have long used methods like life data analysis and reliability growth analysis to make sense of failure data using desktop applications like ReliaSoft’s Weibull++.  This talk introduces ReliaSoft’s Analytics API, a modern ASP.NET Core service that exposes life data analysis and reliability growth analysis through a clean REST surface.  We will also demonstrate Weibull AI, an AI agent layered on top of the Analytics API that allows the user to access the power of the Analytics API's LDA and RGA capabilities through natural conversation.