Effectiveness of MED for Fault Diagnosis in Roller Bearings

Diagnostics of rolling element bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a single algorithm different signal-enhancement and signal-analysis techniques. Among the first ones, Minimum Entropy Deconvolution (MED) has been pointed out as a key tool able to highlight the effect of a possible damage in one of the bearing components within the vibration signal. This paper presents the application of this technique to signals collected on a simple test-rig, able to test damaged industrial roller bearings in different working conditions. The effectiveness of the technique has been tested, comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race and rollers. Since MED performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This represents an original contribution of the paper.

Fetal Alcohol Spectrum Disorder diagnosis and counselling

The Foetal Alcohol Syndrome has long gone unrecognised and undiagnosed in Australia. In the last few years of the 21st Century (2010-14) health practitioners are finally seeking ways of diagnosing the effects of alcohol in pregnancy on the next generation. The author offers a power point presentation which gives guidance on making an accurate diagnosis.

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.

Comparison of high sensitivity troponin T and I assays in the diagnosis of non-ST elevation acute myocardial infarction in emergency patients with chest pain

Objectives: Concentrations of troponin measured with high sensitivity troponin assays are raised in a number of emergency department (ED) patients; however many are not diagnosed with acute myocardial infarction (AMI). Clinical comparisons between the early use (2 h after presentation) of high sensitivity cardiac troponin T (hs-cTnT) and I (hs-cTnI) assays for the diagnosis of AMI have not been reported. Design and methods: Early (0 h and 2 h) hs-cTnT and hs-cTnI assay results in 1571 ED patients with potential acute coronary syndrome (ACS) without ST elevation on electrocardiograph (ECG) were evaluated. The primary outcome was diagnosis of index MI adjudicated by cardiologists using the local cTnI assay results taken ≥6 h after presentation, ECGs and clinical information. Stored samples were later analysed with hs-cTnT and hs-cTnI assays. Results: The ROC analysis for AMI (204 patients; 13.0%) for hs-cTnT and hs-cTnI after 2 h was 0.95 (95% CI: 0.94–0.97) and 0.98 (95% CI: 0.97–0.99) respectively. The sensitivity, specificity, PLR, and NLR of hs-cTnT and hs-cTnI for AMI after 2 h were 94.1% (95% CI: 90.0–96.6) and 95.6% (95% CI: 91.8–97.7), 79.0% (95% CI: 76.8–81.1) and 92.5% (95% CI: 90.9–93.7), 4.48 (95% CI: 4.02–5.00) and 12.86 (95% CI: 10.51–15.31), and 0.07 (95% CI: 0.04–0.13) and 0.05 (95% CI:0.03–0.09) respectively. Conclusions: Exclusion of AMI 2 h after presentation in emergency patients with possible ACS can be achieved using hs-cTnT or hs-cTnI assays. Significant differences in specificity of these assays are relevant and if using the hs-cTnT assay, further clinical assessment in a larger proportion of patients would be required.

Prenatal diagnosis, genetics and reproductive decision-making

Recent developments in genetic science will potentially have a significant impact on reproductive decision-making by adding to the list of conditions which can be diagnosed through prenatal diagnosis. This article analyses the jurisdictional variations that exist in Australian abortion laws and examines the extent to which Australian abortion laws specifically provide for termination of pregnancy on the grounds of fetal disability. The article also examines the potential impact of pre-implantation genetic diagnosis on reproductive decision-making and considers the meaning of reproductive autonomy in the context of the new genetics.

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.

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.

The impact of diabetic ketoacidosis and age on behavior six months post-diagnosis in children with type 1 diabetes

Objectives: Children with type 1 diabetes mellitus (DM1) may be at increased risk of psychosocial and adjustment difficulties. We examined behavioral outcomes six months post-diagnosis in a group of children with newly diagnosed DM1. Methods: This study formed part of a larger longitudinal project examining pathophysiology and neuropsychological outcomes in diabetic patients with or without diabetic ketoacidosis (DKA). Participants were 61 children (mean age 11.8 years, SD 2.7 years) who presented with a new diagnosis of DM1 at the Royal Children’s Hospital, Melbourne. Twenty-three (11 female) presented in DKA and 38 (14 female) without DKA. Parents completed the behavior assessment system for children, second edition six months post-diagnosis. Results: There was a non-linear relationship between age and behavior. Internalising problems (i.e. anxiety depression, withdrawal) peaked in the transition from childhood to adolescence; children aged 10–13 years had elevated rates relative to the normal population (t = 2.55, P = 0.018). There was a non-significant trend for children under 10 to display internalising problems (P = 0.052), but rates were not elevated in children over 13 (P = 0.538). Externalising problems were not significantly elevated in any age group. Interestingly, children who presented in DKA were at lower risk of internalising problems than children without DKA (t = 3.83, P < 0.001). There was no effect of DKA on externalising behaviors. Conclusions: Children transitioning from childhood to adolescence are at significant risk for developing internalising problems such as anxiety and lowered mood after diagnosis of DM1. Somewhat counter-intuitively, parents of children presenting in DKA reported fewer internalising symptoms than parents of children without DKA. These results highlight the importance of monitoring and supporting psychosocial adjustment in newly diagnosed children even when they seem physically well.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

Saliva as an emerging biofluid for clinical diagnosis and applications of MEMS/NEMS in salivary diagnostics

Saliva as a biological fluid is gaining wider acceptance for diagnosing diseases. The growing interest in saliva as a biological fluid is due to its noninvasiveness, ease of use, cost-effectiveness, and multiple sample collection possibilities as well as minimal risk to health care professionals of contracting infectious organisms such as HIV and Hep B. However, the clinical translation of saliva is hampered by our lack of understanding of the biomolecular transportation from blood into saliva, the diurnal variations of biomolecules present in saliva, and relatively low levels of analytes (100th to a 1000th fold less than in blood). We provide information on the current status of salivary research, salivary diagnostics empowered by nanotechnology, and future prospects in this emerging field of saliva diagnostics.