High strain piezoelectric ceramics are the state-of-the-art materials for high precision, positioning devices. A comprehensive review of the latest developments of the various types of perovskite piezoelectric ceramic systems is presented herein, with special attention given to three promising families of lead-free perovskite ferroelectrics: the barium titanate, alkaline niobate and bismuth perovskites. Included in this review are details of phase transition behavior, strain enhancement approaches, material reliabilities as well as the status of some promising applications. This current review describes both compositional and structural engineering approaches that are intended to achieve enhanced strain properties in perovskite piezoelectric ceramics. The factors that affect the strain behavior of high-strain perovskite piezoelectric ceramics are addressed. The reliability characteristics of these high-strain ferroelectrics as well as the recent approaches to the long-term electrical, thermal and time-stability enhancement are summarized. Several promising applications of high-strain perovskite materials are introduced, which take advantages of their characteristics; examples include high-energy storage, pyroelectric and electro-caloric effect and luminescent properties.

Progress in high-strain perovskite piezoelectric ceramics

A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings

The task of condition monitoring and fault diagnosis of rolling element bearing is often cumbersome and labour intensive. Various techniques have been proposed for rolling bearing fault detection and diagnosis. The challenge however, is to efficiently and accurately extract features from signals acquired from these elements, particularly in the time–frequency domain. A new time–frequency technique, known as basis pursuit, was recently developed. This paper presents an application of this new basis pursuit method in the extraction of features from signals collected from faulty rolling bearings with inner race and outer race faults. Results obtained using this new technique were compared with discrete wavelet packet analysis (DWPA) and the matching pursuit technique. Basis pursuit represents features with very fine resolution and sparsity in the time–frequency domain thus rendering easier interpretation of the analysed results. The technique also improves the signal to noise ratio so that subsequent fault detection and identification can be conducted with confidence.

Fault diagnosis of rolling element bearings using basis pursuit

Texture-engineered ceramics enable access to a vast array of novel texture-property relations leading to property values ranging between those of single crystals and isotropic bulk ceramics. Recently developed templated grain growth and magnetic alignment texturing methods yield high quality crystallographic texture, and thus significant advances in achievable texture-engineered properties in magnetic, piezoelectric, electronic, optical, thermoelectric, and structural ceramics. In this paper, we outline the fundamental basis for these texture-engineered properties and review recent contributions to the field of texture-engineered ceramics with an update on the properties of textured lead-free and lead-based piezoelectrics. We propose that further property improvements can be realized through development of processes that improve crystallographic alignment of the grain structure, create biaxial texture, and explore a wider array of crystallographic orientations. There is a critical need to model the physics of texture-engineered ceramics, and more comprehensively characterize texture, thus enabling testing of texture orientation-property relations and materials performance. We believe that in situ measurements of texture evolution can lead to a more fundamental and comprehensive understanding of the mechanisms of texture development.

Texture-engineered ceramics—Property enhancements through crystallographic tailoring

Phase structure and properties of sodium bismuth titanate lead-free piezoelectric ceramics

https://www.research.manchester.ac.uk/portal/files/109871632/EWT.pdf

Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method

Wind turbine blade bearings are often operated in harsh circumstances, which may easily be damaged causing the turbine to lose control and to further result in the reduction of energy production. However, for condition monitoring and fault diagnosis (CMFD) of wind turbine blade bearings, one of the main difficulties is that the rotation speeds of blade bearings are very slow (less than 5 r/min). Over the past few years, acoustic emission (AE) analysis has been used to carry out bearing CMFD. This article presents the results that reflect the potential of the AE analysis for diagnosing a slow-speed wind turbine blade bearing. To undertake this experiment, a 15-year-old naturally damaged industrial and slow-speed blade bearing is used for this study. However, due to very slow rotation speed conditions, the fault signals are very weak and masked by heavy noise disturbances. To denoise the raw AE signals, we propose a novel cepstrum editing method, discrete/random separation-based cepstrum editing liftering (DRS-CEL), to extract weak fault features from raw AE signals, where DRS is used to edit the cepstrum. Thereafter, the morphological envelope analysis is employed to further filter the residual noise leaked from DRS-CEL and demodulate the denoised signal, so the specific bearing fault type can be inferred in the frequency domain. The diagnostic framework combining DRS-CEL and morphological analysis is validated by comparing several methods and related studies, which offers a promising solution for wind-farm applications.

Fault Diagnosis of Industrial Wind Turbine Blade Bearing Using Acoustic Emission Analysis

This article uses acoustic emission (AE) analysis to diagnose an industrial-scale and slow-speed wind turbine blade bearing The main challenge for AE analysis is that the fault signals are mingled with heavy noise As a result, the objective of this article is to filter the raw AE signals and extract weak fault signals To achieve this goal, a general linear and nonlinear auto-regressive (GLNAR) model is first developed to exploit the nonlinear characteristics of the AE signals Then, the sparse augmented Lagrangian (SAL) algorithm is applied to learn the built GLNAR model and filter the raw AE signals The characteristics of SAL is that it is a novel sparse representation technique which can convert the original filtering problem into a number of suboptimization problems, and these subproblems can be solved separately Finally, as the blade bearing rotates at fluctuating speeds, the filtered signals are resampled so that the bearing fault type can be diagnosed in the order domain The proposed diagnostic framework was validated in several experiments under time-varying low-speed and heavy-blade-load conditions The results indicate that our proposed methods are effective and accurate

Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Under Time-Varying Low-Speed and Heavy Blade Load Conditions

Wind turbine blade bearings are pivotal components to pitch blades, which optimize electrical energy output and stop wind turbines for protection. Blade bearing failure can cause the turbine to lose control or even break down. However, due to the very slow rotation speeds (often less than 5 r/min) and limited rotation angles (less than 100 $\rm ^{o}$ ), blade bearings can only produce weak and limited operating condition data, which makes condition monitoring and fault diagnosis very challenging, in particular for naturally damaged conditions. In this article, a naturally damaged large-scale blade bearing, which was in operation on a real wind farm for over 15 years, is investigated. An iterative nonlinear filter is proposed to remove heavy noise and extract weak fault vibration features. Then, the morphological transform-based envelope method is applied to diagnose the bearing fault in the frequency domain. The diagnostic results show that the proposed method can be an effective tool for diagnosing very slow speed blade bearings and is superior to some conventional bearing fault diagnosis methods.

/pdf/naturally-damaged-wind-turbine-blade-bearing-fault-detection-53p8506cge.pdf

Zepeng Liu

Papers

A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings

Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method

Fault Diagnosis of Industrial Wind Turbine Blade Bearing Using Acoustic Emission Analysis

Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Under Time-Varying Low-Speed and Heavy Blade Load Conditions

Naturally Damaged Wind Turbine Blade Bearing Fault Detection Using Novel Iterative Nonlinear Filter and Morphological Analysis