Acoustic emission condition monitoring : an application for wind turbine fault detection

Low speed rotating machines which are the most critical components in drive train of wind turbines are often menaced by several technical and environmental defects. These factors contribute to mount the economic requirement for Health Monitoring and Condition Monitoring of the systems. When a defect is happened in such system result in reduced energy loss rates from related process and due to it Condition Monitoring techniques that detecting energy loss are very difficult if not possible to use. However, in the case of Acoustic Emission (AE) technique this issue is partly overcome and is well suited for detecting very small energy release rates. Acoustic Emission (AE) as a technique is more than 50 years old and in this new technology the sounds associated with the failure of materials were detected. Acoustic wave is a non-stationary signal which can discover elastic stress waves in a failure component, capable of online monitoring, and is very sensitive to the fault diagnosis. In this paper the history and background of discovering and developing AE is discussed, different ages of developing AE which include Age of Enlightenment (1950-1967), Golden Age of AE (1967-1980), Period of Transition (1980-Present). In the next section the application of AE condition monitoring in machinery process and various systems that applied AE technique in their health monitoring is discussed. In the end an experimental result is proposed by QUT test rig which an outer race bearing fault was simulated to depict the sensitivity of AE for detecting incipient faults in low speed high frequency machine.

Isolation and characterization of charge-tagged phenylperoxyl radicals in the gas phase : direct evidence for products and pathways in low temperature benzene oxidation

The phenylperoxyl radical has long been accepted as a critical intermediate in the oxidation of benzene and an archetype for arylperoxyl radicals in combustion and atmospheric chemistry. Despite being central to many contemporary mechanisms underpinning these chemistries, reports of the direct detection or isolation of phenylperoxyl radicals are rare and there is little experimental evidence connecting this intermediate with expected product channels. We have prepared and isolated two charge-tagged phenyl radical models in the gas phase [i.e., 4-(N,N,N-trimethylammonium) phenyl radical cation and 4-carboxylatophenyl radical anion] and observed their reactions with dioxygen by ion-trap mass spectrometry. Measured reaction rates show good agreement with prior reports for the neutral system (k(2)[(Me3N+)C6H4 center dot + O-2] = 2.8 x 10(-11) cm(3) molecule(-1) s(-1), Phi = 4.9%; k(2)[(-O2C)C6H4 center dot + O-2] = 5.4 x 10(-1)1 cm(3) molecule(-1) s(-1), Phi = 9.2%) and the resulting mass spectra provide unequivocal evidence for the formation of phenylperoxyl radicals. Collisional activation of isolated phenylperoxyl radicals reveals unimolecular decomposition by three pathways: (i) loss of dioxygen to reform the initial phenyl radical; (ii) loss of atomic oxygen yielding a phenoxyl radical; and (iii) ejection of the formyl radical to give cyclopentadienone. Stable isotope labeling confirms these assignments. Quantum chemical calculations for both charge-tagged and neutral phenylperoxyl radicals confirm that loss of formyl radical is accessible both thermodynamically and entropically and competitive with direct loss of both hydrogen atom and carbon dioxide.

Production and isolation of ligated metal(IV)-oxo ions by tandem mass spectrometry

High valent metal(IV)-oxo species, \[M(=O)(Melm)(n)(OAc)](+) (M = Mn-Ni, MeIm = 1-methylimidazole, n = 1-2), which are relevant to biology and oxidative catalysis, were produced and isolated in gas-phase reactions of the metal(II) precursor ions \[M(MeIm)(n)(OAc)](+) (M = Mn-Zn, n = 1-3) with ozone. The precursor ions \[M(MeIm)(OAc)](+) and \[M(MeIm)(2)(OAc)](+) were generated via collision-induced dissociation of the corresponding \[M(MeIm)(3)(OAc)](+) ion. The dependence of ozone reactivity on metal and coordination number is discussed. Copyright (C) 2010 John Wiley & Sons, Ltd.

Application of machine learning technique in wind turbine fault diagnosis

In this study, a machine learning technique called anomaly detection is employed for wind turbine bearing fault detection. Basically, the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase. Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method, and a test to failure data of bearings from the NSF I/UCR Center for Intelligent Maintenance Systems (IMS). The latter data set was used to compare anomaly detection with SVM, a previously well-known applied method, in rapidly finding the incipient faults.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

Fault detection of slow speed rolling element bearing with noise removal techniques

Rolling Element Bearings (REBs) are vital components in rotating machineries for providing rotating motion. In slow speed rotating machines, bearings are normally subjected to heavy static loads and a catastrophic failure can cause enormous disruption to production and human safety. Due to its low operating speed the impact energy generated by the rotating elements on the defective components is not sufficient to produce a detectable vibration response. This is further aggravated by the inability of general measuring instruments to detect and process the weak signals at the initiation of the defect accurately. Furthermore, the weak signals are often corrupted by background noise. This is a serious problem faced by maintenance engineers today and the inability to detect an incipient failure of the machine can significantly increases the risk of functional failure and costly downtime. This paper presents the application of noise removal techniques for enhancing the detection capability for slow speed REB condition monitoring. Blind deconvolution (BD) and adaptive line enhancer (ALE) are compared to evaluate their performance in enhancing the source signal with consequential removal of background noise. In the experimental study, incipient defects were seeded on a number of roller bearings and the signals were acquired using acoustic emission (AE) sensor. Kurtosis and modified peak ratio (mPR) were used to determine the detectability of signal corrupted by noise.

Methods for isolation and cultivation of filamentous fungi

Filamentous fungi are important organisms for basic discovery, industry, and human health. Their natural growth environments are extremely variable, a fact reflected by the numerous methods developed for their isolation and cultivation. Fungal culture in the laboratory is usually carried out on agar plates, shake flasks, and bench top fermenters starting with an inoculum that typically features fungal spores. Here we discuss the most popular methods for the isolation and cultivation of filamentous fungi for various purposes with the emphasis on enzyme production and molecular microbiology.

Sensor fault-resilient control of interior permanent-magnet synchronous motor drives

In a conventional ac motor drive using field-oriented control, a dc-link voltage, speed, and at least two current sensors are required. Hence, in the event of sensor failure, the performance of the drive system can be severely compromised. This paper presents a sensor fault-tolerant control strategy for interior permanent-magnet synchronous motor (IPMSM) drives. Three independent observers are proposed to estimate the speed, dc-link voltage, and currents of the machine. If a sensor fault is detected, the drive system isolates the faulty sensor while retaining the remaining functional ones. The signal is then acquired from the corresponding observer in order to maintain the operation of the drive system. The experimental results provided verify the effectiveness of the proposed approach.

Recursive actuator fault detection and diagnosis for emergency landing of UASs

This paper presents a practical recursive fault detection and diagnosis (FDD) scheme for online identification of actuator faults for unmanned aerial systems (UASs) based on the unscented Kalman filtering (UKF) method. The proposed FDD algorithm aims to monitor health status of actuators and provide indication of actuator faults with reliability, offering necessary information for the design of fault-tolerant flight control systems to compensate for side-effects and improve fail-safe capability when actuator faults occur. The fault detection is conducted by designing separate UKFs to detect aileron and elevator faults using a nonlinear six degree-of-freedom (DOF) UAS model. The fault diagnosis is achieved by isolating true faults by using the Bayesian Classifier (BC) method together with a decision criterion to avoid false alarms. High-fidelity simulations with and without measurement noise are conducted with practical constraints considered for typical actuator fault scenarios, and the proposed FDD exhibits consistent effectiveness in identifying occurrence of actuator faults, verifying its suitability for integration into the design of fault-tolerant flight control systems for emergency landing of UASs.

Isolation and characterization of tumor cells from the ascites of ovarian cancer patients : molecular phenotype of chemoresistant ovarian tumors

Tumor cells in ascites are a major source of disease recurrence in ovarian cancer patients. In an attempt to identify and profile the population of ascites cells obtained from ovarian cancer patients, a novel method was developed to separate adherent (AD) and non-adherent (NAD) cells in culture. Twenty-five patients were recruited to this study; 11 chemonaive (CN) and 14 chemoresistant (CR). AD cells from both CN and CR patients exhibited mesenchymal morphology with an antigen profile of mesenchymal stem cells and fibroblasts. Conversely, NAD cells had an epithelial morphology with enhanced expression of cancer antigen 125 (CA125), epithelial cell adhesion molecule (EpCAM) and cytokeratin 7. NAD cells developed infiltrating tumors and ascites within 12-14 weeks after intraperitoneal (i.p.) injections into nude mice, whereas AD cells remained non-tumorigenic for up to 20 weeks. Subsequent comparison of selective epithelial, mesenchymal and cancer stem cell (CSC) markers between AD and NAD populations of CN and CR patients demonstrated an enhanced trend in mRNA expression of E-cadherin, EpCAM, STAT3 and Oct4 in the NAD population of CR patients. A similar trend of enhanced mRNA expression of CD44, MMP9 and Oct4 was observed in the AD population of CR patients. Hence, using a novel purification method we demonstrate for the first time a distinct separation of ascites cells into epithelial tumorigenic and mesenchymal non-tumorigenic populations. We also demonstrate that cells from the ascites of CR patients are predominantly epithelial and show a trend towards increased mRNA expression of genes associated with CSCs, compared to cells isolated from the ascites of CN patients. As the tumor cells in the ascites of ovarian cancer patients play a dominant role in disease recurrence, a thorough understanding of the biology of the ascites microenvironment from CR and CN patients is essential for effective therapeutic interventions.

Condition monitoring and fault diagnosis of diesel engines using instantaneous angular speed analysis

Continuous monitoring of diesel engine performance is critical for early detection of fault developments in an engine before they materialize into a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few nonintrusive condition monitoring techniques that can be utilized for such a task. Furthermore, the technique is more suitable for mass industry deployments than other non-intrusive methods such as vibration and acoustic emission techniques due to the low instrumentation cost, smaller data size and robust signal clarity since IAS is not affected by the engine operation noise and noise from the surrounding environment. A combination of IAS and order analysis was employed in this experimental study and the major order component of the IAS spectrum was used for engine loading estimation and fault diagnosis of a four-stroke four-cylinder diesel engine. It was shown that IAS analysis can provide useful information about engine speed variation caused by changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectra directly associated with the engine firing frequency (at twice the mean shaft rotating speed) can be utilized to estimate engine loading condition regardless of whether the engine is operating at healthy condition or with faults. The amplitude of this order component follows a distinctive exponential curve as the loading condition changes. A mathematical relationship was then established in the paper to estimate the engine power output based on the amplitude of this order component of the IAS spectrum. It was further illustrated that IAS technique can be employed for the detection of a simulated exhaust valve fault in this study.

Controlling electronic and adiabatic isolation of quantum dots from the substrate : an ionization-energy theoretic study

Recent controversy on the quantum dots dephasing mechanisms (between pure and inelastic) is re-examined by isolating the quantum dots from their substrate by using the appropriate limits of the ionization energy theory and the quantum adiabatic theorem. When the phonons in the quantum dots are isolated adiabatically from the phonons in the substrate, the elastic or pure dephasing becomes the dominant mechanism. On the other hand, for the case where the phonons from the substrate are non-adiabatically coupled to the quantum dots, the inelastic dephasing process takes over. This switch-over is due to different elemental composition in quantum dots as compared to its substrate. We also provide unambiguous analysis as to understand why GaAs/AlGaAs quantum dots may only have pure dephasing while InAs/GaAs quantum dots give rise to the inelastic dephasing as the dominant mechanism. It is shown that the elemental composition plays an important role (of both quantum dots and substrate) in evaluating the dephasing mechanisms of quantum dots.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Isolation, structure determination and cytotoxicity studies of tryptophan alkaloids from an Australian marine sponge Hyrtios sp

Mass-guided fractionation of the MeOH extract from a specimen of the Australian marine sponge Hyrtios sp. resulted in the isolation of two new tryptophan alkaloids, 6-oxofascaplysin (2), and secofascaplysic acid (3), in addition to the known metabolites fascaplysin (1) and reticulatate (4). The structures of all molecules were determined following NMR and MS data analysis. Structural ambiguities in 2 were addressed through comparison of experimental and DFT-generated theoretical NMR spectral values. Compounds 1–4 were evaluated for their cytotoxicity against a prostate cancer cell line (LNCaP) and were shown to display IC50 values ranging from 0.54 to 44.9 μM.