This study aims to understand sensors’ behaviour under ageing and its consequences on fault detection: probabilities of false alarm and false silence, which have an impact on maintenance costs and reliability of structures. A literature review is conducted. A drift model is derived from this review and its parameters are analysed. This model is then applied to a real case study. Measurement uncertainties and sensor recalibration strategies are then incorporated into the analysis. A general drift model is derived from the literature review: drift=a+b.(1-e-c.t)+d.t. Its impact on the probabilities of false alarms and missed detections is evaluated: a positive drift will increase false alarms and reduce missed ones. The effect of multiple uncertainty models is also taken into account, and the combined effects of drift and measurement uncertainties are analysed. Building on these results, a probabilistic framework is developed to incorporate the random nature of drift. It is found that a gamma law can explain the evolution of false alarms and silence with this kind of ageing drift. The model was fitted to two available databases and proved to be versatile. It also highlights the wide range of parameter values after fitting, particularly “a” and “d”. The influence of sensor recalibrations on the probabilities of false alarms and missed detections is also assessed: they allow to recover the initial properties of the sensor, reducing the PFA and PFS values by 50%. This study presents several limitations. Firstly, the validation relies on aircraft and digital sensor datasets, as no suitable offshore data sets reporting the temporal evolution of PFA/PFS were identified. Secondly, the probabilistic framework assumes independent Gaussian distributions for drift coefficients, neglecting potential coupling effects or heavy-tailed noise behaviours observed in some harsh environments. Thirdly, recalibrations are modelled as perfect resets, ignoring hysteresis and calibration uncertainties. Lastly, the general drift model, while covering the majority of reviewed sensors, excludes rare polynomial or discontinuous behaviours. Future work will address these constraints by investigating non-Gaussian noise, parameter dependencies and site-specific offshore data. This work offers a critical commercial advantage for the €80bn offshore wind market by directly reducing operations and maintenance costs, which account for 30% of lifetime expenses. The proposed framework transforms sensor ageing data into actionable reliability metrics, enabling operators to eliminate costly false alarms and prevent catastrophic undetected failures. Investment potential lies in three areas: SaaS integration: Licensing as a premium module for Digital Twin and maintenance platforms. Product certification: Empowering sensor manufacturers to validate “Lifetime Reliability”, commanding higher market prices. Risk modelling: Providing insurers with precise data to price offshore assets accurately. This scalable solution promises significant ROI through optimised maintenance schedules and extended asset longevity. This work brings together aspects of sensor science that are typically studied in isolation: measurement uncertainty, drift and fault detection. It also establishes a connection between sensor ageing and the evolution of fault detection performance over time, which, to the authors’ knowledge, has not been explored in this way before. This relationship could be particularly valuable for industries that rely on long-term monitoring, such as civil engineering.
This study aims to address the limitations of contact sensors and the deployment difficulties of deep learning models in measuring the rotational speed of rolling bearing cages. It proposes a lightweight, non-contact visual detection method to achieve robust speed monitoring under complex operating conditions like severe vibration. The method combines customized speckle patterns with a cascaded noise-robust matching algorithm. High-contrast matte speckle markers are applied to the cage to enhance texture. The Scale-Invariant Feature Transform (SIFT) algorithm extracts features and resolves scale drifts caused by axial runout. Following preliminary matching via Fast Library for Approximate Nearest Neighbors (FLANN), the Progressive Sample Consensus (PROSAC) algorithm is used to swiftly eliminate mismatching points induced by oil film reflections. Experimental tests demonstrated the method’s high precision on a test bench. Within a speed range of 500–3000 r/min, the system effectively overcame motion blur, maintaining a maximum relative error between measured and theoretical speeds within 2.39%. The originality lies in the hardware-algorithm synergistic design, avoiding reliance on the massive training data required by deep learning. By integrating customized speckle markers with an optimized PROSAC-based registration framework, this study provides a highly robust, low-cost and easily deployable tool for dynamic performance testing of rolling bearings.
Purpose The aircraft skins are prone to fracturing, leading to serious safety hazards, due to manufacturing defects, overstress, fatigue and harsh environments. Therefore, a method with high reliability, low cost and simple networking was needed to achieve structural life evaluation through continuous monitoring. This paper aims to investigate using a sensing system consisting of densely arranged multiple tags for crack monitoring in aluminum alloy structures. Design/methodology/approach In this paper, a sensing system was composed of four tag arrangements, and the variation of gain and impedance caused by the coupling effect among tags was analyzed via simulation. The reliability of sensor systems for crack detection was studied by constructing a test platform. Findings The simulation results showed that as the crack depth in the coverage area of Tag 1 increased, the real part of the impedance of Tag 1 remained constant. In contrast, the value of the imaginary part tended to increase, while the impedance values of other tags remained unchanged. The gain of Tag 1 decreased, while the gains of the different tags remained unchanged. The test results indicated that the backscattered power tended to decrease as the crack depth increased when a single tag was placed on the surface of the aluminum alloy structure. The backscattered power of Tag 1 increased, while the backscattered power of Tag 4 remained constant when multiple tags were placed. Multiple tags were affixed to the surface of the aluminum alloy structure, leading to mutual coupling between the antennas, which decreased the sensors’ detection sensitivity. Originality/value This paper explores using multiple tags for crack detection in aluminum alloy structures. The consistency of the feature results improves as the relationship between the projection coefficient of the second principal component and the crack depth is established. This paper provides a foundation for utilizing multiple tags for crack detection on the surface of aluminum alloy aircraft skin.
Purpose This review aims to examine wearable sensor-based haptic feedback systems for motor learning and gait reshaping, with emphasis on the closed-loop chain from sensing to behavioral change. Design/methodology/approach This structured narrative review used a transparent search boundary. Literature was identified from PubMed/MEDLINE, Web of Science, Scopus, IEEE Xplore and Google Scholar for work published from January 2000 to May 2026 using combined terms for wearable sensors, haptic or vibrotactile feedback, biofeedback, gait, motor learning, Parkinson disease, stroke, older adults and foot-progression retraining. Studies were included when they reported wearable sensing, closed-loop or cue-based feedback, gait or motor-learning outcomes, validation metrics or deployment constraints; papers were excluded when they were unrelated to gait, lacked wearable sensing/feedback relevance, provided no usable quantitative or architectural information or duplicated a more complete report. Because outcomes and populations were heterogeneous, findings were synthesized as a structured narrative with quantitative comparison tables rather than a pooled meta-analysis. Findings Across the studies summarized in Table 2, the strongest support is for short-term modulation of stance symmetry, thigh extension, gait speed, arm swing and foot-placement targets; reported effects include p = 0.0493 for stroke stance-time symmetry, 14% stride-length and 18% gait-speed increases in older adults, a 229% arm-ROM increase and 2.4 degrees RMS foot-progression-angle tracking error with ICC = 0.94. One six-week knee-osteoarthritis gait-retraining study also reported a 20% reduction in first-peak knee adduction moment and retention of biomechanical and symptom changes at one month. These heterogeneous values do not support a single pooled effect size because sample sizes, comparators, cue schedules and endpoints differ. Sensor accuracy alone is insufficient; repeatability, calibration, latency, comfort, feedback dose, perceptual load, retention and transfer determine whether closed-loop systems remain useful outside the laboratory. Originality/value The review treats wearable haptic gait training as an integrated sensor-feedback-control problem rather than as a catalog of devices.
Purpose - The verticality deviation of trench cutting re-mixing deep-wall (TRD) machines is difficult to accurately evaluate and correct due to heterogeneous geological conditions and unbalanced cutting loads. Conventional inclination monitoring and independent hydraulic cylinder control cannot ensure reliable attitude reconstruction and coordinated actuator correction. This study aims to develop an integrated verticality monitoring and electro-hydraulic control framework for improving attitude evaluation and hydraulic actuator coordination of TRD machines. Design/methodology/approach - A sensing-evaluation-control framework is proposed by integrating distributed attitude measurement, minimum-zone verticality assessment and cross-coupled sliding-mode synchronization control. Multi-node micro-electro-mechanical systems (MEMS) inclination measurements are used to reconstruct the lateral deformation profile of the cutter box. An optimization-based minimum-zone evaluation method is developed to quantify the geometric deviation of the reconstructed profile. Furthermore, a boundary-layer sliding-mode controller with cross-coupled synchronization compensation is designed for dual hydraulic cylinders. Lyapunov analysis demonstrates the boundedness of tracking and synchronization errors under bounded uncertainties and disturbances. Findings - Numerical evaluation of reconstructed cutter-box profiles obtains a minimum-zone envelope width of 6.514 mm, representing the overall geometric deviation range of the reconstructed contour. Experimental results demonstrate improved cylinder tracking performance and reduced synchronization deviation under the proposed control strategy. Dual-cylinder experiments further demonstrate stable coordinated motion under imposed disturbance conditions. Originality/value - This study develops an integrated framework combining distributed sensing, geometric evaluation and coordinated electro-hydraulic control for TRD trenching equipment. The proposed approach provides a potential solution for improving verticality monitoring and actuator coordination under uncertain underground construction conditions.
