In this modern complex world, stress at work is found to be
increasingly a common feature in day to day life. For the same reason, job
stress is one of the active areas in occupational health and safety research for
over last four decades and is continuing to attract researchers in academia and
industry. Job stress in process industries is of concern due to its influence on
process safety, and worker‘s safety and health. Safety in process (chemical and
nuclear material) industry is of paramount importance, especially in a thickly
populated country like India. Stress at job is the main vector in inducing work
related musculoskeletal disorders which in turn can affect the worker health
and safety in process industries. In view of the above, the process industries
should try to minimize the job stress in workers to ensure a safe and healthy
working climate for the industry and the worker. This research is mainly aimed
at assessing the influence of job stress in inducing work related musculoskeletal
disorders in chemical process industries in India
Description:
School of Engineering
Cochin University of Science and Technology
Pramod, V R; Dr. Jagathy Raj, V P(Cochin University of Science and Technology, June , 2007)
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Abstract:
This thesis presents the methodology of linking Total Productive
Maintenance (TPM) and Quality Function Deployment (QFD). The Synergic
power ofTPM and QFD led to the formation of a new maintenance model named
Maintenance Quality Function Deployment (MQFD). This model was found so
powerful that, it could overcome the drawbacks of TPM, by taking care of
customer voices. Those voices of customers are used to develop the house of
quality. The outputs of house of quality, which are in the form of technical
languages, are submitted to the top management for making strategic decisions.
The technical languages, which are concerned with enhancing maintenance
quality, are strategically directed by the top management towards their adoption
of eight TPM pillars. The TPM characteristics developed through the
development of eight pillars are fed into the production system, where their
implementation is focused towards increasing the values of the maintenance
quality parameters, namely overall equipment efficiency (GEE), mean time
between failures (MTBF), mean time to repair (MTIR), performance quality,
availability and mean down time (MDT). The outputs from production system
are required to be reflected in the form of business values namely improved
maintenance quality, increased profit, upgraded core competence, and enhanced
goodwill. A unique feature of the MQFD model is that it is not necessary to
change or dismantle the existing process ofdeveloping house ofquality and TPM
projects, which may already be under practice in the company concerned. Thus,
the MQFD model enables the tactical marriage between QFD and TPM.First, the literature was reviewed. The results of this review indicated that
no activities had so far been reported on integrating QFD in TPM and vice versa.
During the second phase, a survey was conducted in six companies in which
TPM had been implemented. The objective of this survey was to locate any
traces of QFD implementation in TPM programme being implemented in these
companies. This survey results indicated that no effort on integrating QFD in
TPM had been made in these companies. After completing these two phases of
activities, the MQFD model was designed. The details of this work are presented
in this research work. Followed by this, the explorative studies on implementing
this MQFD model in real time environments were conducted. In addition to that,
an empirical study was carried out to examine the receptivity of MQFD model
among the practitioners and multifarious organizational cultures. Finally, a
sensitivity analysis was conducted to find the hierarchy of various factors
influencing MQFD in a company. Throughout the research work, the theory and
practice of MQFD were juxtaposed by presenting and publishing papers among
scholarly communities and conducting case studies in real time scenario.
Description:
School of Engineering,Cochin University of Science and Technology
Archana, R; Dr. R Gopikakumari; Dr. A Unnikrishnan(Cochin University of Science and Technology, March 12, 2015)
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Abstract:
Modeling of chaotic systems, based on the output time series, is quite
promising since the output often represents the characteristic behaviour of
the total system. It has been an interesting topic for researchers over the past
few years. So far, some methods are developed for the identification of
chaotic systems. Because of the intense complexity of chaotic systems, the
performance of existing algorithms is not always satisfactory. Application of
chaotic system theory to socially relevant problems like environmental
studies is the need of the hour Neural networks have the required self-learning capability to tune the
network parameters (i.e. weights) for identifying highly non-linear and
chaotic systems. In the present work, effectiveness of modeling a chaotic
system using dynamic neural networks has been demonstrated. From the rich
literature available for non-linear modeling with neural networks, the
Recurrent Neural Network (RNN) structure is selected. The Extended
Kalman Filter (EKF) algorithm is used to train the RNN. Further, the
Expectation Maximization algorithm is used to effectively arrive at the initial
states and the state covariance. Particle filter algorithm with its two important
variants namely Sampling Importance Resampling (SIR) and Rao
Blackwellised algorithms are also used for training the given RNN. Four
standard chaotic systems, Lorenz, Rossler, Chua and Chen, are modelled
with the three algorithms. The best algorithm is found to be EKF-EM based
on the least mean square error criterion. Validation of RNN model with EKFEM
algorithm is done in time domain by Estimation of embedding
dimension, Phase plots, Lyapunov Exponents, Kaplan -Yorke dimension and
Bifurcation diagrams. Analysis of the chaotic systems is also performed in
the transform domain using Fourier, Wavelet and Mapped Real Transforms.
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Natural chaotic systems are analyzed based on the selected model structure
and training algorithm, taken for analysis. Sunspot, Venice Lagoon and
North Atlantic oscillations are the three of the natural chaotic systems
modelled with the selected RNN model structure and EKF-EM algorithm.
Deepa, Balakrishnan S; Dr.Nandakumar, C G(Cochin University of Science and Technology, January , 2008)
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Abstract:
Frames are the most widely used structural system for multistorey
buildings. A building frame is a three dimensional discrete structure consisting of
a number of high rise bays in two directions at right angles to each other in the
vertical plane. Multistorey frames are a three dimensional lattice structure which
are statically indeterminate. Frames sustain gravity loads and resist lateral forces
acting on it.
India lies at the north westem end of the Indo-Australian tectonic plate and
is identified as an active tectonic area. Under horizontal shaking of the ground,
horizontal inertial forces are generated at the floor levels of a multistorey frame.
These lateral inertia forces are transferred by the floor slab to the beams,
subsequently to the columns and finally to the soil through the foundation system.
There are many parameters that affect the response of a structure to ground
excitations such as, shape, size and geometry of the structure, type of foundation,
soil characteristics etc. The Soil Structure Interaction (SS1) effects refer to the
influence of the supporting soil medium on the behavior of the structure when it
is subjected to different types of loads.
Interaction between the structure and its supporting foundation and soil,
which is a complete system, has been modeled with finite elements. Numerical
investigations have been carried out on a four bay, twelve storeyed regular
multistorey frame considering depth of fixity at ground level, at characteristic
depth of pile and at full depth. Soil structure interaction effects have been studied
by considering two models for soil viz., discrete and continuum. Linear static
analysis has been conducted to study the interaction effects under static load.
Free vibration analysis and further shock spectrum analysis has been conducted to
study the interaction effects under time dependent loads. The study has been
extended to four types of soil viz., laterite, sand, alluvium and layered.The structural responses evaluated in the finite element analysis are
bending moment, shear force and axial force for columns, and bending moment
and shear force for beams. These responses increase with increase in the founding
depth; however these responses show minimal increase beyond the characteristic
length of pile. When the soil structure interaction effects are incorporated in the
analysis, the aforesaid responses of the frame increases upto the characteristic
depth and decreases when the frame has been analysed for the full depth. It has
been observed that shock spectrum analysis gives wide variation of responses in
the frame compared to linear elastic analysis. Both increase and decrease in
responses have been observed in the interior storeys. The good congruence shown
by the two finite element models viz., discrete and continuum in linear static
analysis has been absent in shock spectrum analysis.
Description:
Department of Ship Technology,
Cochin University of Science &
Technology