Nonlinear process fault detection and identification using kernel PCA and kernel density estimation

Kernel principal component analysis (KPCA) is an effective and efficient technique for monitoring nonlinear processes. However, associating it with upper control limits (UCLs) based on the Gaussian distribution can deteriorate its performance. In this paper, the kernel density estimation (KDE) technique was used to estimate UCLs for KPCA-based nonlinear process monitoring. The monitoring performance of the resulting KPCA–KDE approach was then compared with KPCA, whose UCLs were based on the Gaussian distribution. Tests on the Tennessee Eastman process show that KPCA–KDE is more robust and provide better overall performance than KPCA with Gaussian assumption-based UCLs in both sensitivity and detection time. An efficient KPCA-KDE-based fault identification approach using complex step differentiation is also proposed

Dynamic latent variable modelling and fault detection of Tennessee Eastman challenge process

Dynamic principal component analysis (DPCA) is commonly used for monitoring multivariate processes that evolve in time. However, it is has been argued in the literature that, in a linear dynamic system, DPCA does not extract cross correlation explicitly. It does not also give the minimum dimension of dynamic factors with non zero singular values. These limitations reduces its process monitoring effectiveness. A new approach based on the concept of dynamic latent variables is therefore proposed in this paper for extracting latent variables that exhibit dynamic correlations. In this approach, canonical variate analysis (CVA) is used to capture process dynamics instead of the DPCA. Tests on the Tennessee Eastman challenge process confirms the workability of the proposed approach

Nonlinear dynamic process monitoring using kernel methods

The application of kernel methods in process monitoring is well established. How- ever, there is need to extend existing techniques using novel implementation strate- gies in order to improve process monitoring performance. For example, process monitoring using kernel principal component analysis (KPCA) have been reported. Nevertheless, the e ect of combining kernel density estimation (KDE)-based control limits with KPCA for nonlinear process monitoring has not been adequately investi- gated and documented. Therefore, process monitoring using KPCA and KDE-based control limits is carried out in this work. A new KPCA-KDE fault identi cation technique is also proposed. Furthermore, most process systems are complex and data collected from them have more than one characteristic. Therefore, three techniques are developed in this work to capture more than one process behaviour. These include the linear latent variable-CVA (LLV-CVA), kernel CVA using QR decomposition (KCVA-QRD) and kernel latent variable-CVA (KLV-CVA). LLV-CVA captures both linear and dynamic relations in the process variables. On the other hand, KCVA-QRD and KLV-CVA account for both nonlinearity and pro- cess dynamics. The CVA with kernel density estimation (CVA-KDE) technique reported does not address the nonlinear problem directly while the regular kernel CVA approach require regularisation of the constructed kernel data to avoid com- putational instability. However, this compromises process monitoring performance. The results of the work showed that KPCA-KDE is more robust and detected faults higher and earlier than the KPCA technique based on Gaussian assumption of pro- cess data. The nonlinear dynamic methods proposed also performed better than the afore-mentioned existing techniques without employing the ridge-type regulari- sation

The KIT swiss knife gripper for disassembly tasks: a multi-functional gripper for bimanual manipulation with a single arm

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This work presents the concept of a robotic gripper designed for the disassembly of electromechanical devices that comprises several innovative ideas. Novel concepts include the ability to interchange built-in tools without the need to grasp them, the ability to reposition grasped objects in-hand, the capability of performing classic dual arm manipulation within the gripper and the utilization of classic industrial robotic arms kinematics within a robotic gripper. We analyze state of the art grippers and robotic hands designed for dexterous in-hand manipulation and extract common characteristics and weak points. The presented concept is obtained from the task requirements for disassembly of electromechanical devices and it is then evaluated for general purpose grasping, in-hand manipulation and operations with tools. We further present the CAD design for a first prototype.Peer ReviewedPostprint (author's final draft

Statistical process monitoring of a multiphase flow facility

Industrial needs are evolving fast towards more flexible manufacture schemes. As a consequence, it is often required to adapt the plant production to the demand, which can be volatile depending on the application. This is why it is important to develop tools that can monitor the condition of the process working under varying operational conditions. Canonical Variate Analysis (CVA) is a multivariate data driven methodology which has been demonstrated to be superior to other methods, particularly under dynamically changing operational conditions. These comparative studies normally use computer simulated data in benchmark case studies such as the Tennessee Eastman Process Plant (Ricker, N.L. Tennessee Eastman Challenge Archive, Available at 〈http://depts.washington.edu/control/LARRY/TE/download.html〉 Accessed 21.03.2014). The aim of this work is to provide a benchmark case to demonstrate the ability of different monitoring techniques to detect and diagnose artificially seeded faults in an industrial scale multiphase flow experimental rig. The changing operational conditions, the size and complexity of the test rig make this case study an ideal candidate for a benchmark case that provides a test bed for the evaluation of novel multivariate process monitoring techniques performance using real experimental data. In this paper, the capabilities of CVA to detect and diagnose faults in a real system working under changing operating conditions are assessed and compared with other methodologies. The results obtained demonstrate that CVA can be effectively applied for the detection and diagnosis of faults in real complex systems, and reinforce the idea that the performance of CVA is superior to other algorithms

The performance of a 3-Phase Induction Machine under Unbalance Voltage Regime

In industries, electric motors contribute a major percentage of electrical loads, and by implication, a major portion of the generated electrical energy is consumed by electric motors, and it is therefore vital to ensure that these motors operate with negligible energy loss. In operational environment, many factors can be responsible for a reduction in motor performance. One of the major factors is a regime of voltage unbalance that affects the motor, the load and supply network. Three phase induction motor studies can be carried out using methods such as real load test, finite element analysis, flux linkage-current relations etc. A new model based on phase frame analysis has been developed for easy computation of sequence components. This paper seeks to explore the performance of a 3-phase induction motor operating under unbalance voltage conditions using the phase frame analysis. The results of this study established that there are negative impacts of supply imbalance on the performance of three phase motor

Bilateral Laparoscopic Partial Nephrectomies: A Case Report

Although laparoscopy is a recognized operative approach to the management of renal masses, there is currently no standardized approach to manage bilateral synchronous renal masses. We present a case of synchronous bilateral renal masses, identified during work-up for flank pain, and managed simultaneously with laparoscopic partial nephrectomies. The patient is a 42-year-old Caucasian male found to have bilateral renal masses during evaluation for left flank pain. Cross-sectional imaging studies showed a 7.0???7.3???5.2?cm anterior, mid-to-lower pole mass on the left kidney and a 1.5???1.9???1.6?cm medial lower pole mass on the right kidney. He underwent bilateral laparoscopic partial nephrectomy at the same setting, with an uncomplicated postoperative course. Pathology report revealed clear cell renal-cell carcinoma (ccRCC) on both sides. He had normal renal function and no evidence of recurrence in the first 6 months of follow-up. This case demonstrates the possibility and safety of performing bilateral laparoscopic partial nephrectomies in one operative session. Our review of the literature supports the role of genetic counseling and the need for long-term surveillance in young patients having RCC.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140344/1/cren.2015.0007.pd