This article applies an incremental online identification algorithm to develop a set of evolving fuzzy models (FMs) that characterize the nonlinear finger dynamics of the human hand for the myoelectric (ME)-based control of a prosthetic hand. The FM inputs are the ME signals obtained from eight ME sensors and past inputs and/or outputs. The FM outputs are the finger angles, considered here as the midcarpal joint angles, to ensure their control. The best evolving FMs that characterize each of the five fingers are described with the results validated on real data. Simple second-order linear models are next given to enable the cost-effective controller design. Five separate control loops are proposed, with proportional–integral (PI) controllers separately tuned by a frequency-domain approach. Simple PI-fuzzy controllers are designed starting with the linear PI controllers to ensure the control system performance improvement. The evolving FMs are used to simulate accurately the behavior of the human hand. Digital simulation results are included to show the effectiveness of the PI-fuzzy controllers and the performance improvement in comparison to the initial PI ones.

Evolving Fuzzy Models for Prosthetic Hand Myoelectric-Based Control

Integrating IoT with LQR-PID controller for online surveillance and control of flow and pressure in fluid transportation system

Review analysis on cloud computing based smart grid technology in the oil pipeline sensor network system

Digital twin for oil pipeline risk estimation using prognostic and machine learning techniques

The world of oil pipelines is subjected to serious issues due to occurrences of toxic spills, explosions and deformations like particle deposition, corrosions and cracks due to the contact of oil particles with the pipeline surface Hence, the structural integrity of these pipelines is of great interest due to the probable environmental, infrastructural and financial losses in case of structural failure Based on the existing technology, it is difficult to analyze the risks at the initial stage, since traditional methods are only appropriate for static accident analyses Nevertheless, most of these models have used corrosion features alone to assess the condition of pipelines To sort out the above problem in the oil pipelines, fault identification and prediction methods based on K-means clustering and Time-series forecasting incorporated with linear regression algorithm using multiple pressure data are proposed in this paper The real-time validation of the proposed technique is validated using a scaled-down experimental hardware lab setup resembling characteristics exhibited by onshore unburied pipeline in India In the proposed work, crack and blockages are identified by taking pressure rise and pressure drop inferred from two cluster assignment The obtained numerical results from K-means clustering unveils that maximum datasets accumulated range of multiple pressures are within 16147–10638 kg/cm2, 14922–121674 kg/cm2, 27645–12063 kg/cm2 correspondingly Hence by this final cluster center data, inspection engineers able to estimate the normal and abnormal performance of oil transportation in a simple-robust manner The developed forecast model successfully predicts future fault occurrences rate followed by dissimilarity rate from clustering results holds the validity of 919% when applied to the historical pressure datasets The models are expected to help pipeline operators without complex computation processing to assess and predict the condition of existing oil pipelines and hence prioritize the planning of their inspection and rehabilitation

Future prediction & estimation of faults occurrences in oil pipelines by using data clustering with time series forecasting

IoT Based monitoring and control of fluid transportation using machine learning

In order to enhance the proprietary of remote monitoring and control technology, this paper emphasizes the development of IoT based architecture with local intelligent using IMC based PID controller can be integrated with SCADA and this research work spectacle only the development and application of Local Intelligence present in the proposed IoT architecture for remote monitoring and control of concerned field parameters. This proposed local intelligence tactic governs the input and output from a hardware platform positioned on a process plant, organize added processing power for meticulous analysis in the control center by the use of software integration, data acquisition and logging on a created hybrid database, and spectacle significant processed post-data statistics to operators via a standard SCADA. The established local intelligence is experimentally validated in the lab scale experimental fluid transport system to monitor and control pressure and flow rate parameters remotely by incorporating with CENTUM CS 3000. The simulation and experimental results of Local Intelligence using an IMC-PID controller on a lab scale fluid transport system are conveyed with its numerical data.

Local Intelligence for Remote Surveillance and Control of Flow in Fluid Transportation System

Received: 2 February 2020 Accepted: 16 March 2020 Rolling element bearing is one of the important components in rotary machines. Although a significant quantum of work has been done on bearing defect monitoring, estimation of defect size in bearing elements is still a challenge. Vibration signals resulting from rolling element bearing defects, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the fault. The proposed research work is examined under laboratorial set-up keeping rotating speed and or load variation remains unchanged. Hence this paper represents the online monitoring approach of identifying outer, inner race and ball defects in the ball bearing based on the location, count and size of the defects by incorporating PLC with LABVIEW platform. A monitoring tool acquires experimental data from a bearing vibration control test rig. An accelerometer captures the signal from the bearing outer ring then it is processed using PCI-4451 National Instruments data acquisition board and LabVIEW software. To analyze the peak value of the vibration signals during faults, the following 0.5, 1and 2-mm defect size were undertaken. Through real-time experimentation the minimum and maximum range of vibration amplitude values were observed by varying the defect counts as single and multi-nature basis on three zones like inner, outer race and ball of the bearings. Its experimental numerical results reveal that of ball bearing without defects holds values between 10 mV to 16 mV and it also confirms that the ball defects hold higher amplitude vibration on comparison with defects on the inner and outer race vibration signals.

/pdf/vibration-signals-based-bearing-defects-identification-39sz68z2ou.pdf

Vibration Signals Based Bearing Defects Identification Through Online Monitoring Using LABVIEW

This paper aims to explore experimental studies on the NOx removal process by using pilot plant packed column experimental hardware. Physical modeling based on chemical absorption equations is used to estimate the diameter concerning the height and L/G ratio. Hydrogen peroxide is used as the additive for achieving high NOx removal efficiency. The absorbent entering into the packed column has been controlled by varying its flow rate through the fractional order controller. The FOCDM-PIλDµ controller tuning parameters such as KP, τI, τD are determined using CDM (Coefficient Diagram Method) PID control strategy and the additional parameters of FOCDM-PIλDµ controller such as λ and µ are determined based on the PSO algorithm. The comparative analysis is performed with classical controllers like ZN-PID along with the CDM-PID controllers.

/pdf/design-and-experimental-investigations-on-nox-emission-ixp8bt1wh2.pdf

Priyanka E. Bhaskaran

Papers

Future prediction & estimation of faults occurrences in oil pipelines by using data clustering with time series forecasting

IoT Based monitoring and control of fluid transportation using machine learning

Local Intelligence for Remote Surveillance and Control of Flow in Fluid Transportation System

Vibration Signals Based Bearing Defects Identification Through Online Monitoring Using LABVIEW

Design and Experimental Investigations on NOx Emission Control Using FOCDM (Fractional-Order-Based Coefficient Diagram Method)-PIλDµ Controller