Adaptive Prognostics: A reliable RUL approach

In the past decade, data-driven methodologies have gained increasing popularity, offering a foundation for predicting the remaining useful life (RUL) of engineering systems and structures using condition monitoring (CM) data. A particularly intriguing challenge lies in accurately predicting the RUL of systems that exhibit exceptional performance, whether underperforming or overperforming, owing to unforeseen phenomena occurring during their operational life. These unique systems, often referred to as outliers, pose a formidable challenge for RUL prediction. This research addresses this challenge by introducing a novel data-driven model, which is known as the Similarity Learning Hidden Semi-Markov Model (SLHSMM) and extends the capabilities of the Non-Homogeneous Hidden Semi-Markov Model (NHHSMM). The training dataset comprises strain data obtained from open-hole carbon-epoxy specimens exposed solely to fatigue loading. In contrast, the validation-testing dataset includes strain data from two specimens subjected to both fatigue and in-situ impact loading, representing an unexpected and previously unseen event in the training data. The study compares RUL estimations generated by the SLHSMM and NHHSMM. The results indicate that the SLHSMM outperforms the NHHSMM, offering superior accuracy in predicting outliers' RUL. This underscores its capability to adapt to unexpected phenomena and seamlessly incorporate unforeseen data into prognostics.Structural Integrity & Composite

Adaptive prognostics for remaining useful life of composite structures

Prognostics is an emerging field of research that enables the real-time health assessment of an engineering system and the prediction of its future state based on up-to-date information. This field integrates various scientific disciplines including physics/mechanics, computational statistics and probabilistic modeling, machine learning and sensing technologies. The main goal is the prediction of the remaining useful life (RUL) of the engineering system while it is in-service. Lately, there is an effort to study and predict the future status of engineering systems that exhibit a complex degradation process. The availability of condition monitoring (CM) data, the constantly increasing computational power, the development of machine learning algorithms and the advancements on the physics/mechanics for several engineering systems form a solid foundation to achieve that goal. Among the engineering systems that exhibit a complex degradation process are composite structures. Composite structures have made a significant mark in numerous industries, driven by advantages in structural efficiency, performance, versatility and cost. It is well known that the damage accumulation process of composite structures depends on several parameters, i.e. the type of material and the lay-up, the loading frequency and sequence, the manufacturing process. Additionally, the multi-phase nature of composites and the variation of defects result in a stochastic activation of the different failure mechanisms. So, one expects that the long-term behaviour of two comparable composites structures, subjected to comparable environmental and loading conditions, will differ and that makes the fatigue damage analysis, and consequently the prediction of RUL, very complex tasks. This difference is profound especially when unexpected phenomena may occur. The goal of this research is to develop a new RUL prediction model that is able to learn from unexpected phenomena and adapt its parameters accordingly. The model is composed of three elements; 1) sensing techniques to acquire online CM data, 2) machine learning algorithm for developing a damage modelling strategy and 3) stochastic modelling for uncertainty quantification. Based on the literature review, it was concluded that a frequentist data-driven model has the potential to fulfil the research goal and an extension of the Non-Homogenous Hidden Semi Markov model (NHHSMM) is a good candidate. The first step was to design the structure of the RUL prediction model and define its elements. The next step was to develop the extension of the NHHSMM, and verify its correctness and robustness, utilizing simulated Monte-Carlo (MC) data. A series of assumptions was necessary in order to frame the applicability of the model towards composite structures and to achieve an efficient prediction process.Structural Integrity & Composite

A novel approach towards fatigue damage prognostics of composite materials utilizing SHM data and stochastic degradation modeling

A prognostic framework is proposed in order to estimate the remaining useful life of composite materials under fatigue loading based on acoustic emission data and a sophisticated Non Homogenous Hidden Semi Markov Model. Bayesian neural networks are also utilized as an alternative machine learning technique for the non-linear regression task. A comparison between the two algorithms operation, input, output and performance highlights their ability to tackle the prognostic task.Structural Integrity & Composite

Utilizing AE data and stochastic modelling towards fatigue damage diagnostics and prognostics of composites

The procedure of damage accumulation in composite materials, especially during fatigue loading, is a complex phenomenon which depends on a number of parameters such as ply orientation, material properties, geometrical non-linearities etc. Towards condition based health monitoring and decision making, the need not only for diagnostic but also for prognostic tools rises and draws increasing attention the last few years. The damage process is in general hidden and manifests itself through in-situ structural health monitoring (SHM) data. Due to the hidden nature of the damage accumulation, non-homogenous hidden Semi Markov process (NHHSMP) seems to be a suitable candidate for describing adequately the aforementioned system’s degradation in time. Its non-homogeneous aspect takes into account the system’s ageing. Moreover, the sojourn times in each state are assumed to be generally distributed, not necessarily exponentially distributed, which is a more realistic assumption for real world engineering systems. The SHM observations are coming from acoustic emission (AE) data recorded throughout constant amplitude fatigue testing of open-hole carbon/epoxy coupons. The scatter of the cycles to failure reported is quite large, an expected result of the stochasticity in the material properties and material inhomogeneities. A maximum likelihood approach for the estimation of the model parameters is followed and useful diagnostic and prognostic measures such as the coupon's current degradation level as well as measures the coupon's remaining useful life (RUL) are proposed for the monitoring of structural integrity of composite materials.Structural Integrity & Composite

Experimental investigation on the effect of creep on the damage evolution of CFRP structures during fatigue loading

This paper presents an experimental investigation on the effect of creep on the damage evolution of Carbon Fiber Reinforced Polymer structures during fatigue loading. A new experimental campaign is proposed where unidirectional CFRP specimens are tested under the combination of fatigue and constant compressive load. The tests represent the loading that the lower part of an air-wing faces during the flight and parking process. Acoustic Emission technique is employed in order to monitor the damage progression and accumulation. The results of the acoustic emission are compared with reference tests where only fatigue loading is used and it is found that the acoustic emission patterns in terms of number and distribution of events over the duration of tests and energy accumulation is different for these two types of tests. The results indicate that the damage process on CFRP structures is different when creep is present.Structural Integrity & Composite

Extreme prognostics for remaining useful life analysis of composite structures

The procedure of fatigue damage accumulation in composite structures is still unknown and depends on several parameters such as type and frequency of loading, stacking sequence and material properties. Additionally, the nonhomogeneous and anisotropic nature of composites result to a stochastic activation of the different failure mechanisms and make the estimation of remaining useful life (RUL) very complex but interesting task. Data driven probabilistic methodologies have found increasing use the last decade and provide a platform for reliable estimations of RUL utilizing condition monitoring (CM) data. However, the fatigue life of a specific composite structure has a quite significant scatter, with specimens that either underperform or outperform. These specimens are often referred as outliers and the estimation of their RUL is challenging. This study proposes a new RUL probabilistic model, the Extreme Non-Homogenous Hidden Semi Markov Model (ENHHSMM) which is an extension of the Non-Homogenous Hidden Semi Markov Model (NHHSMM). The ENHHSMM uses dynamic diagnostic measures, which are estimated based on the training and testing CM data and adapts dynamically the trained parameters of the NHHSMM. The available CM data are acoustic emission data recorded throughout fatigue testing of open-hole carbon–epoxy specimens. RUL estimations from the ENHHSMM and NHHSMM are compared. The ENHHSMM is concluded as the preferable option since it provides more accurate outlier prognostics.Structural Integrity & Composite

Similarity learning hidden semi-Markov model for adaptive prognostics of composite structures

Data-driven methodologies have found increasing usage in the last decade for remaining useful life (RUL) prognostics of composite materials utilizing structural health monitoring (SHM) data. Of particular interest is the reliable RUL prediction in cases where the end-of-life is not in between the extreme values within the testing dataset. For example, when unexpected phenomena that severely compromise the structural integrity occur during the service life. Such cases are often referred as outliers and the RUL prognosis based on a data-driven model that learns from past data is often erroneous. This study addresses this challenge by proposing a new stochastic model; the Similarity Learning Hidden Semi Markov Model (SLHSMM), an extension of the Non-Homogenous Hidden Semi Markov Model (NHHSMM). Through the utilization of a nonparametric discrete distribution, which characterizes the similarity between the testing structure and the training structures, a dynamic re-estimation process is employed. This process assigns higher importance to the training structures that display greater similarity to the testing one. As a result, the estimated parameters effectively capture the specific characteristics of the testing structure. The training and testing SHM data sets consist of strain measurements collected from a case study where carbon–epoxy single-stringered panels, are subjected to constant, variable, and random amplitude fatigue loading until failure. RUL estimations from the SLHSMM, the NHHSMM, and the Gaussian Process Regression (GPR) are compared. The SLHSMM clearly outperforms its classical counterpart and GPR providing more accurate outlier and inlier prognostics, demonstrating its capability to adapt to unexpected phenomena and integrate unforeseen data into a prognostic platform.Structural Integrity & Composite

In-situ fatigue damage assessment of carbon-fibre reinforced polymer structures using advanced experimental techniques

This study focused on the in-situ fatigue damage assessment of open-hole carbon/epoxy coupons using Acoustic Emission (AE) and Digital Image Correlation (DIC) techniques. Constant amplitude fatigue tests were performed and the main objective was to investigate the damage process, the degradation process of the fatigue modules and to identify features, derived from the experimental data, that can be used as sensitive-to-damage indexes. To this end, the two experimental techniques were reviewed as potential online monitoring tools for the fatigue damage assessment.Structural Integrity & Composite

An adaptive probabilistic data-driven methodology for prognosis of the fatigue life of composite structures

Data driven probabilistic methodologies have found increasing use the last decade and provide a platform for the remaining useful life (RUL) prediction of composite structures utilizing health-monitoring data. Of particular interest is the RUL prediction of composite structures that either underperform or outperform due to unexpected phenomena that might occur during their service life. These composite structures are referred as outliers and the prediction of their RUL is a challenge. This study addresses this challenge by proposing a new data-driven model; the Adaptive Non-Homogenous Hidden Semi Markov Model (ANHHSMM), which is an extension of the NHHSMM. The ANHHSMM uses diagnostic measures, which are estimated based on the training and testing data, and it adapts the trained parameters of the NHHSMM. The training data set consists of acoustic emission data collected from open-hole carbon–epoxy specimens, subjected to fatigue loading, while the testing data set consists of acoustic emission data collected from specimens, subjected to fatigue and in-situ impact loading, which can be considered as an unseen event for the training process. ANHHSMM provides better predictions in comparison to the NHHSMM for all the cases, demonstrating its capability to adapt to unexpected phenomena and integrate unforeseen data to the prognostics course.Structural Integrity & Composite

In-situ impact analysis during fatigue tests of open-hole carbon fibre reinforced polymer specimens

This paper presents the results for an experimental campaign of in-situ impact during tension-tension fatigue loading for open-hole carbon fibre reinforced polymer specimens. High-speed low energy impact was introduced to the specimen with the use of a canon, which was attached to testing bench enabling the impact without the need to remove the specimens from the test bench. Digital Image Correlation, C-scan and Acoustic Emission were utilized to record health monitoring data for damage diagnostics. A strain-based criterion was used to identify a common threshold for the timing of impact ensuring a fair comparison between the different tests. The results indicate that while an impact causes the total amount of damage to increase as one would expect, it does not necessarily increase the damage level in the critical area where final fracture occurs. A dependence on the moment of impact with the fatigue failure was found for specimens subjected to impact before the initiation of the fatigue loading. In contrast, impacting specimens in the presence of fatigue damage had no detrimental effect on the fatigue life, although it was observed that the damaged area was enlarged. Overall, the paper showcases the need to study systemically the effect of in-situ impact on the fatigue life in order to understand better the implications that may be introduced to the integrity of a composite structure.Structural Integrity & Composite