Failure Assessment of Piezoelectric Actuators and Sensors for Increased Reliability of SHM Systems

In the chain from sensing to information extraction, there are many traps where errors can occur, which might lead to false alarms and therefore leave us with the impression of an unreliable system. In this chapter, we deal with the important first element of the chain, the sensor, which can undergo various faults and defects during its lifetime. Especially for the use of acousto-ultrasonic (AU)-based methods or the electro-mechanical impedance (EMI) method, piezoelectric transducers are frequently used. Subsequent steps within the chain of SHM rely on the quality and reliability of these measurements. An overview is given on the usage of piezoelectric transducers within SHM systems, their electro-mechanical coupling and its modeling as well as frequent faults of these devices and methods on how to inspect them and diagnose defects. The authors show the effects of different transducer faults on the excited wave field, used for AU. It is shown how a sensor fault can be detected before the SHM system indicates a (false) alarm. With the help of application scenarios—including temperature variations—the advantages and disadvantages of the introduced methods of transducer inspection are presented, enabling an increased reliability of SHM systems

Fatigue lifetime estimation in structures using ambient vibration measurements

A methodology is proposed for estimating damage accumulation due to fatigue in the entire body of a metallic structure using output-only vibration measurements from a sensor network installed at a limited number of structural locations. Available frequency domain stochastic fatigue methods based on Miner’s damage rule, S-N fatigue cycle curves and Dirlik’s probability distribution of the stress range are used to predict the expected fatigue accumulation of the structure in terms of the power spectral density of the stress processes. In predicting the damage and fatigue lifetime, it is assumed that the unmeasured excitations can be modeled by stationary stochastic processes. The power spectral densities of stresses at unmeasured locations are estimated from the response time history measurements available at the limited measured locations using Kalman filter and a model of the structure. The proposed formulation is demonstrated using a MDOF spring-mass chain model arising from structures that consist of members with uniaxial stress states

Damage detection using robust fuzzy principal component analysis

In this work Robust Fuzzy Principal Component Analysis (RFPCA) is used and compared with comparing with classical Principal Component Analysis (PCA) to detect and classify damages. It has been proved that the RFPCA method achieves better result mainly because it is more compressible than classical PCA and also carries more information, hence not only it can distinguish the healthy structure from the damaged structure much sharper than the traditional counterparts but also in some cases traditional PCA is incapable of discerning the pristine from damaged structure. This work involves experimental results using pipe-like structure powered by a piezoelectric actuators and sensors.Peer ReviewedPostprint (published version

Damage Detection in Metallic Beams from Dynamic Strain Measurements under Different Load Cases by Using Automatic Clustering and Pattern Recognition Techniques

International audienceIn general, the change in the local strain field or global stiffness caused by damage in a structure is very small and the strain field tends to homogenize very quickly in the field close to the defect. Moreover, other environmental effects can fade the slight changes in the strain field. Only by comparing the response of the structure at several points some information about damage may be unveiled. By means of pattern recognition techniques based on the strain field, this task can be achieved. This is the basis of the strain measurements data-driven models. The main limitation of the strain field pattern recognition techniques lies in the susceptibility of the strain field to change depending on the load conditions. In the case of dynamic loads, this may reflect even a greater limitation. Robust automated techniques are required to manage these limitations. In first instance, automatic clustering techniques are needed so that data can be classified according to the load conditions and secondly, a dimensional reduction technique is needed in order to obtain patterns that often underlie from data. Within the context of this paper, a combination of Local Density-based Simultaneous Two-Level (DS2L-SOM) Clustering based on Self-Organizing Maps (SOM) and Principal Components Analysis (PCA) is proposed in order to firstly, classify load conditions and secondly, perform strain field pattern recognition. The clustering technique is the basis for an Optimal Baseline Selection. An experimental validation of the technique is discussed in this paper, comparing damages of different sizes and positions in an aluminum beam, under a set of combined loads under dynamic conditions. Strains were measured at several points by using Fiber Bragg Gratings

A Pattern Recognition Approach for Damage Detection and Temperature Compensation in Acousto-Ultrasonics

International audienceThe global trends in the construction of modern structures require the integration of sensors together with data recording and analysis modules so that their integrity can be continuously monitored for safe-life, economic and ecological reasons. This process of measuring and analysing the data from a distributed sensor network all over a structural system in order to quantify its condition is known as structural health monitoring (SHM). Guided ultrasonic wave-based techniques are increasingly being adapted and used in several SHM systems which benefit from built&#8208,in transduction, large inspection ranges, and high sensitivity to small flaws. However, for reliable health monitoring, much information regarding the innate characteristics of the sources and their propagation is essential. Moreover, any SHM system which is expected to transition to field operation must take into account the influence of environmental and operational changes which cause modifications in the stiffness and damping of the structure and consequently modify its dynamic behaviour. On that account, special attention is paid in this paper to the development of an efficient SHM methodology where robust signal processing and pattern recognition techniques are integrated for the correct interpretation of complex ultrasonic waves within the context of damage detection and identification. The methodology is based on an acousto-ultrasonics technique where the discrete wavelet transform is evaluated for feature extraction and selection, linear principal component analysis for data-driven modelling and self-organizing maps for a two-level clustering under the principle of local density. At the end, the methodology is experimentally demonstrated and results show that all the damages were detectable and i

Damage assessment in a stiffened composite panel using non-linear data-driven modelling and ultrasonic guided waves

Structural components made of composite materials are being used more often in aerospace and aeronautic structures due to their well-known properties such as high mass specific stiffness and strength. However, their application also increases the analysis complexity of such structures. Structural health monitoring (SHM) systems for these structures aim to determine the status of the system in real time such that a longer safe life and lower operational costs can be guaranteed. On that account, this paper is concerned with the experimental validation of a structural health monitoring methodology where a damage detection and classification scheme based on an acousto-ultrasonic (AU) approach is applied to a composite panel incorporating stiffening elements using a piezoelectric active sensor network in conjunction with time-frequency multiresolution analysis and non-linear feature extraction. Therefore, structural dynamic responses from the simplified aircraft composite skin panel are collected and signal features are then extracted with a signal processing and data fusion methodology in terms of the wavelet transform technique and hierarchical non-linear principal component analysis. A critical comparison with linear feature extraction methods indicates that the proposed method outperforms the traditional linear methods for the purpose of damage classification. Additionally, results show that all the damages were detectable and classifiable, and the selected features proved capable of separating all damage conditions from the undamaged state.Postprint (published version

Highly sensitive electromechanical piezoresistive pressure sensors based on large-area layered PtSe2_{2} films

Two-dimensional (2D) layered materials are ideal for micro- and nanoelectromechanical systems (MEMS/NEMS) due to their ultimate thinness. Platinum diselenide (PtSe$_{2}$), an exciting and unexplored 2D transition metal dichalcogenides (TMD) material, is particularly interesting because its scalable and low temperature growth process is compatible with silicon technology. Here, we explore the potential of thin PtSe$_{2}$ films as electromechanical piezoresistive sensors. All experiments have been conducted with semimetallic PtSe$_{2}$ films grown by thermally assisted conversion of Pt at a CMOS-compatible temperature of 400{\deg}C. We report high negative gauge factors of up to -84.8 obtained experimentally from PtSe$_{2}$ strain gauges in a bending cantilever beam setup. Integrated NEMS piezoresistive pressure sensors with freestanding PMMA/PtSe$_{2}$ membranes confirm the negative gauge factor and exhibit very high sensitivity, outperforming previously reported values by orders of magnitude. We employ density functional theory (DFT) calculations to understand the origin of the measured negative gauge factor. Our results suggest PtSe$_{2}$ as a very promising candidate for future NEMS applications, including integration into CMOS production lines.Comment: 33 pages, 5 figures, including supporting information with 10 figure

Inspection of Piezoceramic Transducers Used for Structural Health Monitoring

The use of piezoelectric wafer active sensors (PWAS) for structural health monitoring (SHM) purposes is state of the art for acousto-ultrasonic-based methods. For system reliability, detailed information about the PWAS itself is necessary. This paper gives an overview on frequent PWAS faults and presents the effects of these faults on the wave propagation, used for active acousto-ultrasonics-based SHM. The analysis of the wave field is based on velocity measurements using a laser Doppler vibrometer (LDV). New and established methods of PWAS inspection are explained in detail, listing advantages and disadvantages. The electro-mechanical impedance spectrum as basis for these methods is discussed for different sensor faults. This way this contribution focuses on a detailed analysis of PWAS and the need of their inspection for an increased reliability of SHM systems

Identification Procedures as Tools for Fault Diagnosis of Rotating Machinery

System identification procedures offer the possibility to correct erroneous models, based on measurement data. Recently, this conventional field of application is being extended to fault detection and system diagnosis. In contrast to conventional approaches, identification procedures try to establish an unequivocal relation in between the damage and specific mechanical parameters, based on a suitable model. Furthermore, they can be employed during normal operation of the machinery. In this paper, several identification procedures on the basis of the Extended Kalman Filter are introduced and employed for model-based fault detection. Their feasability is proved by several examples. First, it is shown that the crack depth of a simulated Jeffcott-rotor can be calculated correctly. Then, the procedures are utilized to determine the crack depth of a rotor test rig. Finally, it is proved that identification procedures can be employed for the determination of unbalances without having to apply test masses