A control-theoretical fault prognostics and accommodation framework for a class of nonlinear discrete-time systems

Fault diagnostics and prognostics schemes (FDP) are necessary for complex industrial systems to prevent unscheduled downtime resulting from component failures. Existing schemes in continuous-time are useful for diagnosing complex industrial systems and no work has been done for prognostics. Therefore, in this dissertation, a systematic design methodology for model-based fault prognostics and accommodation is undertaken for a class of nonlinear discrete-time systems. This design methodology, which does not require any failure data, is introduced in six papers. In Paper I, a fault detection and prediction (FDP) scheme is developed for a class of nonlinear system with state faults by assuming that all the states are measurable. A novel estimator is utilized for detecting a fault. Upon detection, an online approximator in discrete-time (OLAD) and a robust adaptive term are activated online in the estimator wherein the OLAD learns the unknown fault dynamics while the robust adaptive term ensures asymptotic performance guarantee. A novel update law is proposed for tuning the OLAD parameters. Additionally, by using the parameter update law, time to reach an a priori selected failure threshold is derived for prognostics. Subsequently, the FDP scheme is used to estimate the states and detect faults in nonlinear input-output systems in Paper II and to nonlinear discrete-time systems with both state and sensor faults in Paper III. Upon detection, a novel fault isolation estimator is used to identify the faults in Paper IV. It was shown that certain faults can be accommodated via controller reconfiguration in Paper V. Finally, the performance of the FDP framework is demonstrated via Lyapunov stability analysis and experimentally on the Caterpillar hydraulics test-bed in Paper VI by using an artificial immune system as an OLAD --Abstract, page iv

A Model Based Fault Detection and Prognostic Scheme for Uncertain Nonlinear Discrete-Time Systems

A new fault detection and prognostics (FDP) framework is introduced for uncertain nonlinear discrete time system by using a discrete-time nonlinear estimator which consists of an online approximator. A fault is detected by monitoring the deviation of the system output with that of the estimator output. Prior to the occurrence of the fault, this online approximator learns the system uncertainty. In the event of a fault, the online approximator learns both the system uncertainty and the fault dynamics. A stable parameter update law in discrete-time is developed to tune the parameters of the online approximator. This update law is also used to determine time to failure (TTF) for prognostics. Finally a fourth order translational oscillator with rotating actuator (TORA) system is used to demonstrate the fault detection while a mass damper system is used for demonstrating the prognostics scheme

An Online Approximator-Based Fault Detection Framework for Nonlinear Discrete-Time Systems

In this paper, a fault detection scheme is developed for nonlinear discrete time systems. The changes in the system dynamics due to incipient failures are modeled as a nonlinear function of state and input variables while the time profile of the failures is assumed to be exponentially developing. The fault is detected by monitoring the system and is approximated by using online approximators. A stable adaptation law in discrete-time is developed in order to characterize the faults. The robustness of the diagnosis scheme is shown by extensive mathematical analysis and simulation results

A Model Based Fault Detection Scheme for Nonlinear Multivariable Discrete-Time Systems

In this paper, a novel robust scheme is developed for detecting faults in nonlinear discrete time multi-input and multi-output systems in contrast with the available schemes that are developed in continuous-time. Both state and output faults are addressed by considering separate time profiles. The faults, which could be incipient or abrupt, are modeled using input and output signals of the system. By using nonlinear estimation techniques, the discrete-time system is monitored online. Once a fault is detected, its dynamics are characterized using an online approximator. A stable parameter update law is developed for the online approximator scheme in discrete-time. The robustness, sensitivity, and performance of the fault detection scheme are demonstrated mathematically. Finally, a Continuous Stir Tank Reactor (CSTR) is used as a simulation example to illustrate the performance of the fault detection scheme

Click Stream Data Analysis Using Hadoop

The objective of this project is to collect Click Stream data of USA Government websites which is high in volume and velocity, and store it for analysis in a cost effective manner for enhanced insight and decision making. I expect to learn how to process this data in an engineer’s way. I have plenty of tools in my hand like map reduce, pig, streaming and many more. But for a given business case it is very important to know which tools should be used to achieve the objective. In brief this is what I expect to learn. The Hadoop-ecosystem, State-of-the-art in Big Data age is perfectly suitable for click stream data analysis. To achieve the objective mentioned, it is very much necessary to have scalable systems at low cost which can operate at great speeds and bring out wonderful insights. Perfect answer for this is Hadoop

Character recognition using tesseract enabling multilingualism.

Character recognition builds a recognizing factor for identifying the accuracy in characters. The accuracy of classifying the recognizing characters in an image is applied through deep learning methods. The character recognition is mainly focusing on the layers of text recognition through deep learning techniques. Well cleared python code assists to furnish all the levels of image by following deep learning that algorithmically analyse and recognize text from the given input image. This research work has been proposed for recognizing characters using deep learning techniques and recognize the input image with well-furnished and most efficient output. It provides a high level of accuracy-built output after the recognition of characters in the high-resolution image. This recognized character can be converted into user desired languages where the proposed model is trained to recognize some particular languages

Detection of image forgery for forensic analytics.

Due to the technical revolution in digital image processing, different advanced image manipulation software has been used in recent years to produce new unrealistic images without leaving evidence of what is happening in the world, so it would be difficult to detect tampering visually. Digital image forgeries have many techniques, but it is still very difficult to identify copy-move forgery. Therefore, we use a robust algorithm in this paper to detect copy-move forgery based on the descriptor speed-up robust feature (SURF) as a key-point detection, high-pass filtering as a matching feature, nearest neighbor used as a clustering algorithm to divide the entire image. By swapping the matched feature points with the corresponding super pixel blocks, the doubtful regions are identified, and then, the corresponding blocks are combined on the basis of similar local color features (LCF). Finally, to obtain the suspected forged areas, morphological close operation was applied. The results of the study indicate that the proposed method achieves considerable output based on key-point detection compared to other forgery detection methods used in the current method in order to address the research challenges

Preclinical evaluation of nephroprotective potential of a probiotic formulation LOBUN on Cyclosporine-A induced renal dysfunction in Wistar rats

The aim of present study was to evaluate the nephroprotective effect of probiotic formulation LOBUN on Cyclosporine A (CsA) induced renal dysfunction in Wistar rats. CsA (20 mg/kg body weight s.c) was administered for 15 days to cause renal dysfunction in Wistar rats. The probiotic formulation LOBUN was administered with the dose of 500 mg/kg body weight (p.o) for twice (TGI) and thrice a day (TGII). The samples were analyzed for the parameters like blood urine nitrogen (BUN), serum creatinine, serum uric acid, total serum protein and urine proteins, urine potassium, urine sodium. The renal functional and histopathological studies revealed that the oral administration of probiotic formulation LOBUN has provided appreciable renoprotection and possibly alleviated the symptoms of Chronic Kidney Disease (CKD) at the dose of 500 mg/kg body weight administered thrice a day and also the results were supported by histopathological findings

The effect of disocclusion time-reduction therapy to treat chronic myofascial pain: A single group interventional study with 3 year follow-up of 100 cases

Purpose: The purpose of this study is to evaluate the longevity of reduced disclusion time in treating and removing myofascial pain dysfunction symptoms following the T-Scan-based, immediate complete anterior guidance development (ICAGD) coronoplasty. This measured occlusal adjustment has been shown to reduce the muscle hyperactivity of myofascial pain. Methods: Myofascial pain symptomatic patients were recruited as per the diagnostic criteria for temporomandibular disorders (TMDs), including the clinical protocol and assessment instruments outlined by the international RDC/TMD consortium network (version: January 20, 2014) to assess the efficacy of reduced disclusion time in left and right lateral excursions to resolve the myofascial pain symptoms. As per the inclusion and exclusion criteria, 100 cases were treated with ICAGD in three visits, each 1 week apart. Recall disclusion time measurements were recorded every 3 months over 3 years. The RDC/TMD questionnaire was used for symptom assessment at every recall visit. ICAGD brought pretreatment prolonged disclusion time down to <0.4 s, as quantified from T-Scan force and time data records, while the subjects were assessed for symptom relief. The Wilcoxon signed-rank test was used for statistical analysis (P < 0.05). Results: Changes in the intensity of many symptoms from reducing the disclusion time to <0.4 s were statistically significant from treatment day 1, and onward through the 3-year period of observation (P < 0.05). Conclusion: The results indicate that ICAGD reduces the musculoskeletal symptoms of myofascial pain, such that this methodology increases clinical therapeutic success

Disclusion time reduction therapy in treating occluso-muscular pains

Disclusion time reduction (DTR) is an objective treatment protocol using T-Scan III (digital analysis of occlusion) and electromyography for treating occlusally activated orofacial pains. Chronic occluso-muscle disorder is a myogenous subset of temporomandibular disorder symptoms. These muscular symptoms are induced within hyperactive masticatory muscles due to prolonged disclusion time, occlusal interferences, and occlusal surface friction that occur during mandibular excursive movements. This case report describes a patient treated by DTR therapy, whereby measured pretreatment prolonged disclusion time was reduced to short disclusion time using the immediate complete anterior guidance development enameloplasty, guided by T-Scan occlusal contact time and force analysis synchronized with electromyographic recordings of four masticatory muscles