In this work, an attempt has been made to develop an automated system for detecting electroclinical seizures such as tonic-clonic seizures, complex partial seizures, and electrographic seizures (EGSZ) using higher-order moments of scalp electroencephalography (EEG). The scalp EEGs of the publicly available Temple University database are utilized in this study. The higher-order moments, namely skewness and kurtosis, are extracted from the temporal, spectral, and maximal overlap wavelet distributions of EEG. The features are computed from overlapping and non-overlapping moving windowing functions. The results show that the wavelet and spectral skewness of EEG is higher in EGSZ than in other types. All the extracted features are found to have significant differences (p < 0.05), except for temporal kurtosis and skewness. A support vector machine with a radial basis kernel designed using maximal overlap wavelet skewness yields a maximum accuracy of 87%. In order to improve the performance, the Bayesian optimization technique is utilized to determine the suitable kernel parameters. The optimized model achieves the highest accuracy of 96% and an MCC of 91% in three-class classification. The study is found to be promising, and it could facilitate the rapid identification process of life-threatening seizures.

Accurate detection of seizure types is one of the important research in the field of epilepsy for effective treatment and management. The identification of potential biomarkers is difficult and challenging as the nonlinear and nonstationary characteristics of the electroencephalogram (EEG) vary within a seizure type and among the seizure types. In this work, a novel variational mode decomposition (VMD)-based feature fusion approach is proposed to detect the evolution of seizures into electrographic seizures (EGSZs), complex partial seizures (CPSZs), and focal to bilateral tonic–clonic seizures (TCSZs). The scalp EEGs of these seizures are decomposed using VMD. The features, namely, zero crossing (ZC), Shannon entropy (ShEn), approximate entropy (ApEn), and root squared zeroth moment (RSZM), are extracted from band-limited oscillatory modes. These features are employed to design a three-class classification model using random forest. In order to enhance the performance further, a fusion of RSZM measures is adapted. The proposed framework is experimented with ictal EEGs of Temple University and Hospital (TUH) database. The results indicate that the six-level VMD performs better in terms of differentiating seizure types. Among the features, the RSZM is found to effectively recognize the seizure types from the first few seconds of ictal EEG. Furthermore, the fusion of RSZM results with a maximum accuracy (ACC) of 96.91% for tenfold cross validation and 85.2% for patient-wise cross validation. In addition, the performance of our approach is also found to be promising for the differentiation of ictal and normal EEGs when we tested on wider datasets. Therefore, the proposed framework has great potential in epilepsy diagnosis.

Epilepsy is the most common chronic neurologic disorder characterized by the recurrence of unprovoked seizures. These seizures are paroxysmal events that result from abnormal neuronal discharges and are categorized into various types based on the clinical manifestations and localization. Tonic-Clonic seizures (TCSZ) may lead to injuries, and constitute the major risk factor for sudden unexpected death in epilepsy (SUDEP), especially in unattended patients. Therapeutic decisions and clinical trials rely on Video EEG which is not practical outside of clinical setting. In this study, wavelet entropy of scalp EEG signals are utilized to discriminate the seizures with and without clinical manifestations. The scalp EEG records from the publically available Temple University Hospital (TUH) dataset are considered for this work. A seven-level, fourth order Daubechies (db4) wavelet is utilized for the decomposition of first four seconds of scalp EEG during seizures. The entropy is extracted from the resultant coefficients and are used to develop SVM based models. Most of the extracted features found to have significant differences (p<0.05). The results show that polynomial SVM model achieves an accuracy of 95.5%, positive predictive value (PPV) of 99.4%, negative predictive value (NPV) of 91.57% and F-Score of 95.9%. Therefore, the proposed approach could be a support in detecting life-threatening seizures.

Epilepsy is a disabling and devastating neurological disorder, characterized by recurrent seizures. These seizures are caused by the abrupt disturbance of the brain and are categorized into various types based on the clinical manifestations and localization. Seizures with clinical manifestations require immediate medical attention. In this work, an attempt has been made to differentiate the seizures with and without clinical manifestations using wavelet energy of scalp EEG signals. For this purpose, scalp EEG records from the publically available Temple University Hospital (TUH) database are considered in this work. The first four seconds of scalp EEG during seizure is subjected to seven-level Daubechies (db4) wavelet decomposition and energy is extracted from the resultant coefficients. These features are used to develop k-Nearest Neighbor (k-NN) classification model for the detection. The results show that the energy associated with most of the sub-bands exhibits significant difference (p<0.05) in these two types of seizures. It is found that the machine learning model based on k-NN achieves an accuracy of 87.6% and precision of 87.3%. Therefore, it appears that the proposed approach could aid in detecting life-threatening seizures in clinical settings.

BACKGROUNDThe best treatment for patients with advanced non–small cell lung cancer (NSCLC) and a poor performance status is not well defined. In this phase 2 trial, patients were randomized to receive treatment with either single‐agent pemetrexed or 1 of 2 combination regimens.METHODSPatients with newly diagnosed, histologically confirmed nonsquamous NSCLC and an Eastern Cooperative Oncology Group (ECOG) performance status of 2 were stratified by age and serum albumin level and were randomized (1:1:1) to 1 of 3 regimens: pemetrexed (arm 1), pemetrexed and bevacizumab (arm 2), or pemetrexed, carboplatin, and bevacizumab (arm 3). The response to treatment was assessed every 2 cycles; responding and stable patients continued treatment until progression or unacceptable toxicity.RESULTSOne hundred seventy‐two patients were randomized, 162 patients began the study treatment, and 146 patients completed 2 cycles and were evaluated for their response. The median progression‐free survival (PFS) was 2.8 months in arm 1, 4.0 months in arm 2, and 4.8 months in arm 3. The overall response rates were 15% in arm 1, 31% in arm 2, and 44% in arm 3. The overall survival was similar in the 3 treatment arms. All 3 regimens were relatively well tolerated. Patients receiving bevacizumab had an increased incidence of hypertension, proteinuria, and bleeding episodes, but most events were mild or moderate.CONCLUSIONSAll 3 regimens were feasible for patients with advanced NSCLC and an ECOG performance status of 2. The addition of bevacizumab to pemetrexed increased the overall response rate. The efficacy of pemetrexed/carboplatin/bevacizumab (median PFS, 4.8 months) approached the prespecified study PFS goal of 5 months. Larger studies will be necessary to define the role of bevacizumab in addition to standard pemetrexed and carboplatin in this population. Cancer 2018;124:1982‐91. © 2018 American Cancer Society.

This phase-2 trial evaluated the efficacy of axitinib as maintenance therapy for patients with metastatic colorectal cancer (mCRC) following first-line treatment with FOLFOX/bevacizumab. Patients with mCRC received mFOLFOX/bevacizumab followed by axitinib maintenance after four cycles. The primary endpoint was progression-free survival (PFS). Seventy patients were enrolled. Common treatment-related toxicities were fatigue, nausea, diarrhea, and peripheral neuropathy during FOLFOX/bevacizumab treatment; and fatigue, hypertension, diarrhea, and peripheral neuropathy during axitinib treatment. Median PFS was 8.3months. Treatment with FOLFOX/bevacizumab followed by maintenance axitinib as first-line treatment for mCRC produced a median PFS consistent with historical controls of other first-line regimens.

Replacement of deteriorated water pipes is a capital-intensive activity for utility companies. Replacement planning aims to minimize total costs while maintaining a satisfactory level of service and is usually conducted for individual pipes. Scheduling replacement in groups is seen to be a better method and has the potential to provide benefits such as the reduction of maintenance costs and service interruptions. However, developing group replacement schedules is a complex task and often beyond the ability of a human expert, especially when multiple or conflicting objectives need to be catered for, such as minimization of total costs and service interruptions. This paper describes the development of a novel replacement decision optimization model for group scheduling (RDOM-GS), which enables multiple group-scheduling criteria by integrating new cost functions, a service interruption model, and optimization algorithms into a unified procedure. An industry case study demonstrates that RDOM-GS can improve replacement planning significantly and reduce costs and service interruptions.

Background: AUY922 is an inhibitor of heat shock protein 90 (Hsp90). Hsp90 inhibitors induce kit degradation in preclinical gastrointestinal stromal tumor (GIST) models. This trial was designed to determine the progression-free survival (PFS) of patients with GIST refractory to or intolerant of imatinib and sunitinib. Methods: Eligible patients received AUY922 70mg/mg(2) by intravenous (IV) infusion on days 1, 8, and 15 of 21-day cycles. Treatment continued until progression or unacceptable toxicity. Results: Between December 2011 and January 2015, 25 patients were enrolled (median age, 63years; 56% male) and received a median of 2 cycles (range: 1-12) of AUY922 treatment. Thirty-four patients were planned, but enrollment was stopped early due to slow accrual. Median PFS was 3.9months (95% CI: 2.5, 5.3) and median OS was 8.5months (95% CI: 5.2, 16.7). Radiographic response was evaluated in 21 patients; one patient achieved PR (4%) with another 15 having best response of stable disease (60%). The most common treatment-related adverse event was diarrhea (60% all grades). Reversible ocular toxicities that resulted in drug hold (24%) or reduction (8%) were also observed. Conclusion: AUY922 produced a median PFS which compares favorably to historical controls of placebo (6weeks) for patients refractory to treatment with imatinib. While diarrhea and ocular toxicities were common, the majority of patients received treatment until disease progression.

This paper presents an experimental study on the vibration signal patterns associated with a simulated piston slap test of a four-cylinder diesel engine. It is found that a simulated worn-off piston results in an increase in vibration root-mean-square (RMS) peak amplitudes associated with the major mechanical events of the corresponding cylinder (i.e., inlet and exhaust valve closing and combustion of Cylinder 1). This then led to an increase of overall vibration amplitude of the time-domain statistical features such as RMS, crest factor, skewness, and kurtosis in all loading conditions. The simulated worn-off piston not only increased the impact amplitude of piston slap during the engine combustion, but also produced a distinct impulse response during the air induction stroke of the cylinder attributing to an increase of lateral impact force as a result of piston reciprocating motion and the increased clearance between the worn-off piston and the cylinder. The unique signal patterns of piston slap disclosed in this paper can be utilized to assist in the development of condition monitoring tools for automated diagnosis of similar diesel engine faults in practical applications.

An effective prognostics program will provide ample lead time for maintenance engineers to schedule a repair and to acquire replacement components before catastrophic failures occur. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique. For comparative study of the proposed model with the proportional hazard model (PHM), experimental bearing failure data from an accelerated bearing test rig were used. The result shows that the proposed prognostic model based on health state probability estimation can provide a more accurate prediction capability than the commonly used PHM in bearing failure case study.

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 non-intrusive 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 field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under non-linear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under non-stationary and non-linear conditions are also discussed.

This paper presents a group maintenance scheduling case study for a water distribution network. This water pipeline network presents the challenge of maintaining aging pipelines with the associated increases in annual maintenance costs. The case study focuses on developing an effective pipeline replacement planning for the water utility. Replacement planning involves large capital commitment and can be difficult as it needs to balance various replacement needs under limited budgets. A Maintenance Grouping Optimization (MGO) model based on a modified genetic algorithm was utilized to develop an optimum group maintenance schedule over a 20 year cycle. An adjacent geographical distribution of pipelines was used as a grouping criterion to control the searching space of the MGO model through a Judgment Matrix. Based on the optimum group maintenance schedule, the total cost was effectively reduced compared with the schedules without grouping maintenance jobs. This optimum result can be used as a guidance to optimize the current maintenance plan for the water utility.

Continuous monitoring of diesel engine performance is critical for early detection of fault developments in the engine before they materialize and become a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few non-intrusive condition monitoring techniques that can be utilized for such tasks. In this experimental study, IAS analysis was employed to estimate the loading condition of a 4-stroke 4-cylinder diesel engine in a laboratory condition. It was shown that IAS analysis can provide useful information about engine speed variation caused by the changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectrum directly associated with the engine firing frequency (at twice the mean shaft revolution speed) can be utilized to estimate the engine loading condition regardless of whether the engine is operating at normal running conditions or in a simulated faulty injector case. The amplitude of this order component follows a clear exponential curve as the loading condition changes. A mathematical relationship was established for the estimation of the engine power output based on the amplitude of the major order component of the measured IAS spectrum.

Engineering Asset Management 2010 represents state-of-the art trends and developments in the emerging field of engineering asset management as presented at the Fifth World Congress on Engineering Asse

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

The availability of bridges is crucial to people’s daily life and national economy. Bridge health prediction plays an important role in bridge management because maintenance optimization is implemented based on prediction results of bridge deterioration. Conventional bridge deterioration models can be categorised into two groups, namely condition states models and structural reliability models. Optimal maintenance strategy should be carried out based on both condition states and structural reliability of a bridge. However, none of existing deterioration models considers both condition states and structural reliability. This study thus proposes a Dynamic Objective Oriented Bayesian Network (DOOBN) based method to overcome the limitations of the existing methods. This methodology has the ability to act upon as a flexible unifying tool, which can integrate a variety of approaches and information for better bridge deterioration prediction. Two demonstrative case studies are conducted to preliminarily justify the feasibility of the methodology

The availability of bridges is crucial to people’s daily life and national economy. Bridge health predication plays an important role in bridge management because maintenance optimization is implemented based on prediction results of bridge deterioration. Conventional bridge deterioration models can be categorised into two groups, namely condition states models and structural reliability models. Optimal maintenance strategy should be carried out based on both condition states and structural reliability of a bridge. However, none of existing deterioration models considers both condition states and structural reliability. This study thus proposes a Dynamic Objective Oriented Bayesian Network (DOOBN) based method to overcome the limitations of the existing methods. This methodology has the ability to act upon as a flexible unifying tool, which can integrate a variety of approaches and information for better bridge deterioration prediction. Two demonstrative case studies are conducted to preliminarily justify the feasibility of the methodology.