Chemweno P.,Catholic University of Leuven |
Pintelon L.,Catholic University of Leuven |
Van Horenbeek A.,Catholic University of Leuven |
Muchiri P.,Dedan Kimathi University of Technology
International Journal of Production Economics | Year: 2015
Risk assessment performs a critical decision support role in maintenance decision making. This is through assisting maintenance practitioners systematically identify, analyze, evaluate and mitigate equipment failures. Often, such failures are mitigated through formulating effective maintenance strategies. In asset maintenance, well-known risk assessment techniques include the Failure Mode and Effect Analysis (FMEA), Fault Tree Analysis (FTA), and Bayesian Networks (BN). In recent years, considerable research attention has been directed towards improving existing techniques, often at the expense of a structured framework for selecting suitable risk assessment techniques. Often, several criteria influence the selection process. Moreover, the criteria are closely linked to specific organizational competencies that vary from one firm to another. In this study, a selection methodology for risk assessment techniques in the maintenance decision making domain is proposed. In the methodology, generic selection criteria for the FMEA, FTA and BN are derived based on the risk assessment process outlined in the ISO 31000:2009 standard. The criteria are prioritized using the Analytic Network Process (ANP), taking into account the judgment and opinion of academic and industrial domain experts. The results illustrate the usefulness of the proposed methodology towards assisting maintenance practitioners discern important competencies relevant to the specific technique and as such select the technique best suited for the organization. © 2015 Elsevier B.V.
Zamankhan P.,Dedan Kimathi University of Technology |
Zamankhan P.,University of KwaZulu - Natal
Mathematical Problems in Engineering | Year: 2015
The air-water mixture from an artificially aerated spillway flowing down to a canyon may cause serious erosion and damage to both the spillway surface and the environment. The location of an aerator, its geometry, and the aeration flow rate are important factors in the design of an environmentally friendly high-energy spillway. In this work, an analysis of the problem based on physical and computational fluid dynamics (CFD) modeling is presented. The numerical modeling used was a large eddy simulation technique (LES) combined with a discrete element method. Three-dimensional simulations of a spillway were performed on a graphics processing unit (GPU). The result of this analysis in the form of design suggestions may help diminishing the hazards associated with cavitation. © 2015 Piroz Zamankhan.
Wa Maina C.,Dedan Kimathi University of Technology
IEEE AFRICON Conference | Year: 2015
Biodiversity monitoring is important in assessing the state of an ecosystem and determining if conservation actions are required. This is particularly important when conservation resources are scarce. However, traditional methods of biodiversity monitoring are labour intensive and cannot be applied in every ecosystem where there is need. In order to expand the application of biodiversity monitoring, there is need to automate this important task. In this work we present an application of audio diarization methods for biodiversity monitoring and show how these methods can be used to measure the abundance of indicator taxa in areas of interest. The use of audio recordings has the potential to reduce the time and effort spent in biodiversity monitoring. The experiments are performed on a freely available dataset of bird song recordings with the birds serving as indicator taxa in the ecosystem of interest. We are able to estimate the number of bird species in the recordings and this information can be used to estimate the species richness in an ecosystem. © 2015 IEEE.
Chemweno P.K.,Center for Industrial Management Traffic and Infrastructure |
Pintelon L.,Center for Industrial Management Traffic and Infrastructure |
Muchiri P.N.,Dedan Kimathi University of Technology
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2015
In today's industries, optimal spare parts provisioning plays a critical role towards sustaining the asset's operational capabilities. In this context, spare parts pooling is increasingly mentioned as a plausible approach for optimizing spare parts management, more so for repairable systems. In this sense, several frameworks are proposed in literature, though largely analytical, thus limiting their capabilities with respect to modeling complex repairable systems. Moreover, the nature of interactions often account for aspects such as system reliability, imperfect component reconditioning, repair capacity and spare parts inventory that is multi-echelon in nature. To realistically model such complexities, simulation approaches are often explored. This paper presents a discrete event simulation modeling study. The simulation model mimics the impact of several aspects on unreliable repairable systems. The aspects include the repair capacity, component reconditioning process, and multi-echelon spare part provisioning strategy. Moreover, the influence of spare parts quality aspects is evaluated and demonstrated in the case study of critical system for a thermal power plant. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Wa Maina C.,Dedan Kimathi University of Technology |
Muhia A.,Jomo Kenyatta University of Agriculture and Technology |
Opondo J.,Dedan Kimathi University of Technology
2016 IST-Africa Conference, IST-Africa 2016 | Year: 2016
This paper presents low cost laboratories that aim to enhance the teaching of electrical and electronic engineering. The laboratories have been designed and developed based on the Raspberry Pi microprocessor system and are aimed at exposing students to the integration of software and hardware in electrical engineering in addition to ensuring that students appreciate the theoretical foundations of areas such as digital signal processing and instrumentation. The paper describes the laboratory exercises developed. Feedback from students was collected and analysed. The positive response shows the effectiveness of this approach in teaching electrical and electronic engineering. © 2016 IIMC.
Maina C.W.,Dedan Kimathi University of Technology
2015 IST-Africa Conference, IST-Africa 2015 | Year: 2015
Kenya's rich biodiversity faces a number of threats including human encroachment, poaching and climate change. Since Kenya is a developing country, there is need to manage the sometimes competing interests of development, such as infrastructure development, and conservation. To achieve this, tools to effectively monitor the state of Kenya's various ecosystems are essential. In this paper we propose a biodiversity monitoring software tool that integrates acoustic indices of biodiversity, recognition of species of interest based on their vocalizations and acoustic census. This tool can be used by non-experts to determine the current state of their ecosystems by monitoring the state of bird species that serve as indicator taxa and whose abundance is related to the abundance of other terrestrial vertebrates including the 'big five'. The tool we propose exploits state-of-the art advances in signal processing and machine learning to perform biodiversity monitoring, bird species detection and census in a joint framework. Using publicly available data we demonstrate how current acoustic indices of biodiversity can be improved by incorporating machine learning based audio segmentation algorithms. We also show how open source toolkits can be used to build bird species recognition systems. Code to reproduce the experiments in this paper is available on Github at https://github.com/ciiram/BirdPy. © 2015 IIMC International Information Management Corporation Ltd.
Kiplangat D.C.,Kerala University |
Kiplangat D.C.,Dedan Kimathi University of Technology |
Asokan K.,College of Engineering, Trivandrum |
Kumar K.S.,Kerala University
Renewable Energy | Year: 2016
Simple linear methods are widely used for time series modelling and prediction and in particular for the forecast of wind speed variations. Linear prediction models are popular for their simplicity and computational efficiency, but their prediction accuracy generally deteriorates beyond a few time steps. In this paper we demonstrate that the prediction accuracy of simple auto-regressive (AR) models can be significantly improved, by as much as 60.15% for day-ahead predictions and up to 18.25% for week-ahead predictions, when combined with suitable time series decomposition. The comparison with new reference forecast model (NRFM) also shows similar accuracy gain of week ahead predictions. The combined model is capable of forecasting wind speed up to 7 days ahead with an average root mean square error less than 3 m/s. We also compare the performance of AR and f-ARIMA models in wind speed prediction and observe that the f-ARIMA model is no better than the AR model when used in combination with time series decomposition. © 2016 Elsevier Ltd.
Muchiri P.N.,Dedan Kimathi University of Technology |
Pintelon L.,Center for Industrial Management |
Martin H.,Open University Nederland |
Chemweno P.,Center for Industrial Management
International Journal of Production Research | Year: 2014
Equipment maintenance and system reliability are important factors affecting the organisations ability to provide quality and timely services to customer. While maintenance remains an important function to manufacturing, it is only recently that attempts have been made to quantify its impact on equipment performance. In this research, an approach of linking maintenance with equipment performance is developed using simulation modelling. The modelling approach involves defining probabilistic models and assumptions affecting system performance, such as: the probabilistic model for the initial failure rate/intensity of the equipment; the probabilistic model for the system deterioration and corresponding effect; the probabilistic model for the random times of corrective maintenance (CM) and preventive maintenance (PM) that takes into the account the types of maintenance plans/policies and the potential dependency between CM and PM times; and the probabilistic model for the random effects of CM and PM on the reliability of the equipment. Using a continuous manufacturing equipment, the model is used to analyse the impact of deterioration, failures and maintenance (policies, timing and efficiency) on equipment performance. It is shown that modelling the effect maintenance provides a basis of evaluating maintenance efforts with the potential application in performance evaluation and decision support while investigating opportunities for manufacturing equipment performance improvement. © 2013 © 2013 Taylor & Francis.
PubMed | Ornithology Section and Dedan Kimathi University of Technology
Type: | Journal: Biodiversity data journal | Year: 2016
Environmental degradation is a major threat facing ecosystems around the world. In order to determine ecosystems in need of conservation interventions, we must monitor the biodiversity of these ecosystems effectively. Bioacoustic approaches offer a means to monitor ecosystems of interest in a sustainable manner. In this work we show how a bioacoustic record from the Dedan Kimathi University wildlife conservancy, a conservancy in the Mount Kenya ecosystem, was obtained in a cost effective manner. A subset of the dataset was annotated with the identities of bird species present since they serve as useful indicator species. These data reveal the spatial distribution of species within the conservancy and also point to the effects of major highways on bird populations. This dataset will provide data to train automatic species recognition systems for birds found within the Mount Kenya ecosystem. Such systems are necessary if bioacoustic approaches are to be employed at the large scales necessary to influence wildlife conservation measures.We provide acoustic recordings from the Dedan Kimathi University wildlife conservancy, a conservancy in the Mount Kenya ecosystem, obtained using a low cost acoustic recorder. A total of 2701 minute long recordings are provided including both daytime and nighttime recordings. We present an annotation of a subset of the daytime recordings indicating the bird species present in the recordings. The dataset contains recordings of at least 36 bird species. In addition, the presence of a few nocturnal species within the conservancy is also confirmed.
PubMed | Red Cross, Moi University, Kenya Methodist University and Dedan Kimathi University of Technology
Type: Journal Article | Journal: International journal of palliative nursing | Year: 2016
Globally, life-threatening diseases are on the rise, indicating the need for palliative care, an approach of ensuring quality of life for the patient and his or her family. Education and training is one of the ways of ensuring staff competency in providing palliative care.This study sought to explore challenges faced by both public and private faith-based training institutions offering palliative care.This was a qualitative study in both public and private training institutions, which were selected randomly. Six institutions were selected for the study. One palliative care trainer in each institution was interviewed using a semi-structured interview guide. Information was recorded, transcribed and thematic analysis was done. Data were presented in the form of narration.Many institutions had incorporated palliative care into their curricula. However, these institutions faced challenges, including few allocated hours and few members of staff trained in palliative care. Clinical area employees were not well equipped with knowledge on palliative care.Challenges exist in both public and private institutions. Institutions should allow for more training hours in palliative care.