98

References

[1]. Ahuja I.P.S., and Khamba J.S. (2008), ‘Strategies and success factors for

overcoming challenges in TPM implementation in Indian manufacturing

industry’, Journal of Quality in Maintenance Engineering, Vol.14, No.2,

pp.123–147.

[2]. Aiwina Heng, Sheng Zhang, Andy C.C. Tan and Joseph Mathew (2009),

‘Rotating machinery prognostics: State of the art, challenges and opportunities’,

Mechanical Systems and Signal Processing, Vol.23, No.3, pp.724–739.

[3]. Arsecularatne J.A., Fowle R.F., Mathew P., and Oxley P.L.B. (1996),

‘Prediction of tool life in oblique machining with nose radius tools’, Wear,

Vol.198, No.1–2, pp.220–228.

[4]. Ahmari A.M.A. (2007), ‘Predictive machinability models for a selected hard

material in turning operations’, Journal of Materials Processing Technology,

Vol.190, No.1–3, pp.305–311.

[5]. Alireza Ghasemi, Soumaya Yacout, and M.-Salah Ouali (2010), ‘Evaluating

the Reliability Function and the Mean Residual Life for Equipment w ith

Unobservable States’, IEEE Transactions on Reliability, Vol.59, No.1, pp.45–

54.

[6]. Altab Hossain, Ataur rahman, Mohiuddin A.K.M and Yulfian Aminanda

(2012), ‘Fuzzy logic system for tractive performance prediction of an intelligent

air-cushiontrack vehicle’, International Journal of Aerospace and Mechanical

Engineering, Vol.6, No.1, pp.1–7.

[7]. Al-Habaibeh A., Zorriassatine F., and Gindy N. (2002), ‘Comprehensive

experimental evaluation of a systematic approach for cost effective and rapid

design of condition monitoring systems using Taguchi’s method’, Journal of

Materials Processing Technology, Vol.124, No.3, pp.372–383.

[8]. Ball P.D., Despeisse, Evans S., and Levers A. (2009), ‘Mapping Manufacturing

material, energy and waste process flows’, Proceedings of the 7th global

conference on sustainable manufacturing, Chennai, India.

99

[9]. Baruah P., and Chinnam R.B. (2005), ‘HMM for diagnostics and prognostics in

machining process’, International journal of production research, Vol.43, No.6,

pp.1275–1293.

[10]. Babur Ozcelik and Mahmut Bayramoglu (2006), ‘The statistical modeling of

surface roughness in high-speed flat end milling’, International Journal of

Machine Tools and Manufacture, Vol.46, No.12–13, pp.1395 –1402.

[11]. Bradley D.M., and Gupta R.C. (2003), ‘Limiting behavior of the mean residual

life’, Annals of the Institute of Statistical Mathematics, Vol.55, No.1, pp 217–

226.

[12]. Bom Soon Lee, Han sub Chung, Kim T, Ford F.P and Andersen P.L(1999),

‘Remaining life prediction methods using operating data and knowledge on

mechanisms’, Journal of Nuclear Engineering and Design, Vol.191, No.2,

pp.157-165.

[13]. Bhuie A.K., Ogunseitan O.A., Saphores J-D.M., Shapiro A.A. (2004),

‘Environmental and economic trade-offs in consumer electronic products

recycling: A case study of cell phones and computers’, Proceedings of the

I E E E International Symposium on Electronics and the Environment, pp.74 –

79.

[14]. Cao H-J., Liu F., He Y., and Zhang H. (2002), ‘Study on model set based

process planning strategy for green manufacturing’, Computer Integrated

Manufacturing System ,Vol.8, No. 12, pp.978–982.

[15]. Carnero M.C. (2005), ‘Selection of diagnostic techniques and instrumentation

in a predictive maintenance program. A case study’, Decision Support Systems,

Vol.38, No.4, pp.539–555.

[16]. Chee Keong Tan, Phil Irving and David Mba (2007), ‘A comparative

experimental study on the diagnostic and prognostic capabilities of acoustics

emission, vibration and spectrometric oil analysis for spur gears’, Mechanical

Systems and Signal Processing, Vol.21, No.1, pp. 208–233.

[17]. ChaochaoChen, Bin Zhang, Vachtsevanos G., and Orchard M. (2011),

‘Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-

Order Particle Filtering’, IEEE Transations on Industrial Electronics, Vol.58,

No.9, pp.4353–4364.

100

[18]. Chaochao Chen, Bin Zhang and Vachtsevanos G. (2012), ‘Prediction of

Machine Health Condition Using Neuro-Fuzzy and Bayesian Algorithms’, IEEE

Transactions on Instrumentation and Measurement, Vol.61, No.2, pp.297–306.

[19]. Coetzee J.L. (1997), ‘Maintenance’, Hatfield, South Africa: Maintenance

Publishers.

[20]. Carl S., Byington P.E, Mattew J Watson., and Edwards D. (2004), ‘Data -

Driven neural network methodology to remaining life predictions for aircraft

actuator components’, Proceedings of the IEEE Aerospace conference, Vol.6,

pp.3581–3589.

[21]. Choudhury S.K., and Bartarya G. ( 2003), ‘Role of temperature and surface

finish in predicting tool wear using neural network and design of experiment’,

International Journal of Machine Tools & Manufacture, Vol.43, No.7, pp.747–

753.

[22]. Christian N. Madu , Chuhua Kuei , Ifeanyi E Madu , (2002), ‘A hierarchic

metric approach for integration of green issues in manufacturing: a paper

recycling application’, Journal of Environmental Management, Vol.64, No.3,

pp. 261–272.

[23]. Chun-Hsin Wu, Jan-Ming Ho and Lee D.T. (2004), ‘Travel-time prediction

withSupport vector regression’, IEEE Transactions on intelligent

transportation systems, Vol.5, No.4, pp.276–281.

[24]. Chiang Hong and Wei-Chiang (2006), ‘Predicting Engine Reliability by

Support Vector Machines’ International Journal of Advanced Manufacturing

Technology, Vol. 28, Nos. 1-2, pp. 154-161

[25]. Divya Tomar, Ruchi Arya and sonali Agarwal, (2011), ‘Prediction of

profitability of industries using weighted SVR’, International Journal of

Computer Science and Engineering, Vol.3, No.5, pp.1938–1944.

[26]. Diaz N., Helu M., Jayanathan S., Yifen Chen Horvath A., and Dornfeld D.

(2010), ‘Environmental analysis of milling machine tool use in various

manufacturing environments’, Proceedings of the IEEE International

Symposium on Sustainable Systems and Technology, pp.1–6.

101

[27]. Das A.N., and Sarmah S.P. (2010), ‘Preventive replacement models: an

overview and their application in process industries’, European Journal of

Industrial Engineering, Vol.4, No.3, pp.280–307.

[28]. Diego J. Pedregal and Maria Carmen Carnero (2009), ‘Vibration analysis

diagnostics by continuous-time models: A case study’, Reliability Engineering

and System Safety, Vol.94, No.2, pp.244–253.

[29]. Daniel Kirby E., and Joseph C Chen, (2007), ‘Development of a fuzzy-nets-

based surface roughness prediction system in turning operations’, Computers &

Industrial Engineering, Vol.53, No.1, pp. 30–42.

[30]. Enrico Zio and Francesco Di Maio (2010), ‘A data driven fuzzy approach for

predicting the remaining useful life in dynamic failure scenarios of a nuclear

system’, Reliability engineering and system safety, Vol.95, No.1, pp. 49 – 57.

[31]. Erry Yulian T Adesta, Muataz AI Hazza, Muhammad Riza, Delvis Agusman

and Rosehan (2010), ‘Tool life estimation model based on simulated flank wear

during high speed hard turning’, European Journal of Scientific Research,

Vol.39, No. 2, pp. 265–278.

[32]. Fatida Rugrungruang, Sami Kara and Hartmut Kaebernic (2009), ‘An

integrated methodology for assessing physical and technological life of products

for reuse’, International Journal of Sustainable Manufacturing, Vol.1, No.4,

pp.463–490.

[33]. Fatida Rugrungruang (2008), ‘An Integrated Methodology for Assessing

Physical & Technological life of Products for Reuse’, Thesis, University of new

South Wales.

[34]. Fagang Zhao, Jin Chen, Lei Guo and Xinglin Li (2009), ‘Neuro-fuzzy Based

Condition Prediction of Bearing Health’, Journal of Vibration and Control,

Vol.15, No.7, pp.1079–1091.

[35]. Feldmann K and Melzer K (2004), ‘Implementation of E-waste Legislation in

EU Members states,North America and China’, Proceedings of the 11 th

International CIRP life cycle Engineering seminar on Product Life Cycle-

Quality Management, Belgrade,Serbia, pp. 49-56.

102

[36]. Grall A., Bérenguer C., Dieulle L. ( 2002), ‘A condition-based maintenance

policy for stochastically deteriorating systems’, Reliability Engineering &

System Safety, Vol.76, No.2, pp.167–180.

[37]. Griese H., Poetter H., Schischke K., Ness O., and Reichl H. (2004), ‘Reuse

and lifetime extension strategies in the context of technology innovations,

global markets, and environmental legislation’, Proceedings of the IEEE

International Symposium on Electronics and the Environment, pp.173–178.

[38]. Guide V.D.R., Jayaraman V., Linton J.D. (2002), ‘Building contingency

planning for closed-loop supply chains with product recovery’, Journal of

Operations Management, Vol.21, No.3, pp.259–279.

[39]. Grall A., Dieulle L., Berenguer C., and Roussignol M. (2002 b),

‘Continuous-time predictive-maintenance scheduling for a deteriorating system’,

IEEE Transactions on Reliability, Vol.51 , No.2, pp.141–150 .

[40]. Gang Niu and Bo-Suk Yang (2010), ‘Intelligent condition monitoring and

prognostics system based on data-fusion strategy’, Expert Systems with

Applications, Vol.37, No.12, pp.8831–8840.

[41]. Gebraeel N.Z., Lawley M.A., Li R., and Ryan J.K. (2005), ‘Residual-life

distributions from component degradation signals: A Bayesian approach’, IIE

Transactions, Vol.37, No.6, pp. 543–557.

[42]. Gungor.A and Gupta S.M (1999), ‘Issuses in environmentally conscious

manufacturing and product recovery: a survey’, Computers and Industrial

Engineering, Vol.36, No.4, pp.811-853.

[43]. Haitao Liao, Wenbiao Zhao and Huairui Guo (2006), ‘Predicting remaining

useful life of an individual unit using proportional hazards model and logistic

regression model’, Proceedings of IEEE Reliability and maintenance

symposium, pp.127–132.

[44]. Herzog M.A., Marwala T., and Heyns P.S., (2009), ‘Machine and component

residual life estimation through the application of neural networks’, Reliability

Engineering and System Safety, Vol.94, No.2, pp.479–489.

[45]. Hua Liu , Weiping Chen , Zhixin Kang , Tungwai Ngai , and Yuanyuan Li

(2005), ‘Fuzzy Multiple Attribute Decision Making for Evaluating Aggregate

103

Risk in Green Manufacturing’, Tsinghua Science & Technology,Vol.10, No.5,

pp.627–632.

[46]. Hongzhou Wang (2002), ‘A survey of maintenance policies of deteriorating

systems’, European Journal of Operational Research, Vol.139, No.3, pp.469–

489.

[47]. Huang S.N., Tan K.K., and Lee T.H. (2007), ‘Automated Fault Detection and

Diagnosis in Mechanical Systems’, IEEE Transactions on Systems, Man, and

Cybernetics, Part C: Applications and Reviews, Vol.37, No.6, pp.1360–1364.

[48]. Hong Thom Pham and Bo-Suk Yang (2010), ‘Estimation and forecasting of

machine health condition using ARMA/GARCH model’, Mechanical Systems

and Signal Processing, Vol. 24, No.2, pp.546–558.

[49]. Inman D.J., Farrar C.R., and Lopes V. (2005), Damage prognosis - For

aerospace, civil and mechanical systems, John Wiley & Sons.

[50]. Iman Attarzadeh and Siew Hock Ow (2010), ‘A Novel Algorithmic Cost

Estimation Model Based on Soft Computing Technique’, International Journal

of Computer Science, Vol.6, No.2, pp.117–125.

[51]. ISO 3685 (1993), ‘Tool life testing with single point turning tools’, 2nd

edition,

International Organization for Standards, Geneva.

[52]. Jack pouchet (2010), ‘Reuse IT to promote sustainability, efficiency’, Energy

and environmental news for business, news letter,

www.environmentalleader.com .

[53]. Jay L. Devore. (2008), ‘Probability and Statistics for Engineers’, Cengage

Learning.

[54]. Jaharah A Ghani, Fifdaus Mohamad Hamzah, Mohd. Nizam Ab. Rahman and

Baba Md. Deros (2006), ‘The reliability of tool life prediction model in end

milling’, Journal Mekanikal, Vol.21, pp.65–71.

[55]. Jaklistsch F. (1983), ‘Metal cutting Technology’, Valeron, Berkly.

104

[56]. Jardine A.K.S and Banjevic(2006), ‘A review on machinery diagnostics and

prognostics implementing condition-based maintenance’, Mechanical Systems

and Signal Processing , Vol.20 , No.7, pp. 1483–1510.

[57]. Jihong Yan, Dingguo Hua and Xing wang (2011), ‘Sustainable manufacturing

oriented prognosis for facility reuse’, Key engineering materials, Vol.450,

pp.437–440.

[58]. Jiri Vass, Robert B Randall, Sami Kara and Hartmut Kaebernick (2010),

‘Vibration based approach to life time prediction of electric motors for reuse’,

International Journal of Sustainable Manufacturing, Vol.2, No.1, pp.2–29.

[59]. Jiju Antony, Raj bardhan Anand,Maneesh Kumar and Tiwari M.K. (2006),

‘Multiple response optimization using Taguchi methodology and nero-fuzzy

based model’, Journal of Manufacturing Technology Management, Vol.17,

No.7, pp. 908–925.

[60]. Jutta Gutberlet (2000), ‘Sustainability: a new paradigm for industrial

production’, International Journal of Sustainability in Higher Education, Vol.1,

No.3, pp.225–236.

[61]. Jiang and Zhigang (2006), ‘A vector projection method to evaluating machine

tool alternatives for green manufacturing’, Proceedings of the International

Conference on Technology and Innovation, pp.640–643.

[62]. Jay Lee, Jun Ni, Dragan Djurdjanovic, Hai Qiu and Haitao Liao, (2006),

‘Intelligent prognostics tools and e-maintenance’, Computers in Industry,

Vol.57, No.6, pp.476– 489.

[63]. Jae Hong Suh, Soundar R.T. Kumara and Shreesh P. Mysore ( 1999),

‘Machinery Fault Diagnosis and Prognosis: Application of Advanced Signal

Processing Techniques’, CIRP Annals - Manufacturing Technology, Vol.48,

No.1, pp. 317–320.

[64]. Jihong Yan and Jay Lee (2007), ‘A Hybrid Method for On-line Performance

Assessment and Life Prediction in Drilling Operations’, Proceedings of the

IEEE International Conference on Automation and Logistics, Jinan, pp.2500–

2505.

105

[65]. Jihong Yan, Muammer Koç and Jay Lee (2004), ‘A prognostic algorithm for

machine performance assessment and its application’, Production Planning &

Control: The Management of Operations , Vol.15, No. 8, pp.796–801.

[66]. Jesuthanam C.P., Kumanan S., and Asokan P. (2007), ‘Surface roughness

prediction using hybrid neural networks’, Machining Science and Technology:

An International Journal , Vol. 11, No.2, pp.271–286.

[67]. Jones B., Jenkinson I., Yang Z., and Wang J. (2010), ‘The use of Bayesian

network modelling for maintenance planning in a manufacturing industry’,

Reliability Engineering and System Safety, Vol.95, No.3, pp.267–277.

[68]. Kanari N., Pineau J-L., and Shallari S. (2003), ‘End-of-life vehicle recycling

in the European Union’, Journal of the Minerals, Metals and Materials

Society,Vol.55, No.5, pp.15-19.

[69]. Kara S., and Li W. (2011), ‘Unit process energy consumption models for

material removal processes’, CIRP Annals - Manufacturing Technology,

Vol.60, No.1, pp.37–40.

[70]. Kara S., Mazhar MI., Kaebernick H and Ahmed H. (2005), ‘Determining the

reuse potential of components based on life cycle data’, Annals of the CIRP,

Vol. 54,No.1, pp. 154-161.

[71]. Kyoung-Jae Kim (2003), ‘Financial time series forecasting using support

vector machines’, Neurocomputing, Vol.55, No.(1–2), pp.307–319.

[72]. Krishnan N., and Sheng P.S. (2000), ‘Environmental versus Conventional

Planning for Machined Components’, CIRP Annals - Manufacturing

Technology, Vol.49, No.1, pp.363–366.

[73]. Klausner M., Grimm W., a n d Hendrickson M.C. (1998), ‘Reuse of

electric motors in consumer products’, Journal of Industrial Ecology, Vol.2,

No.2, pp.89–102.

[74]. Kaebernick H., Anityasari M., and Kara S. (2002), ‘A Technical and

economic model for end-of-life options of industrial products’, International

Journal of Environment and Sustainable Development, Vol.1, No.2, pp. 171–

183.

106

[75]. Kaiser K.A., and Gebraeel N.Z. (2009), ‘Predictive Maintenance Management

Using Sensor-Based Degradation Models’, IEEE Transactions on Systems, Man

and Cybernetics, Part A: Systems and Humans, Vol.39, No.4, pp.840–849.

[76]. Kocijan J., and Tanko V. (2011), ‘Prognosis of gear health using Gaussian

process model’, Proceedings of the IEEE International Conference on

Computer as a Tool, pp.1–4.

[77]. Lajis M.A., Mustafizul Karim A.N., Nurul Amin A.K.M., Hafiz A.M.K., and

Turnad L.G. (2008), ‘Prediction of tool life in end milling of hardened steel

AISID2’, European Journal of Scientific Research, Vol.4, pp.592–602.

[78]. Levitin G. (2005), ‘Universal generating function in reliability analysis and

optimization’, London, Springer-Verlag.

[79]. Lee K.C., Ho S.J., and Ho S.Y. (2005), ‘Accurate estimation of surface

roughness from Texture features of the surface image using an adaptive nero-

fuzzy inference system’, Precision Engineering, Vol.29, No.1, pp. 95–100.

[80]. Lee J. (1997), ‘Strategy and challenges on remote diagnostics and maintenance

for manufacturing equipment’, Annual Proceedings of Reliability and

Maintainability Symposium, Virginia, pp.368–370.

[81]. Li C.J., and Lee H. (2005), ‘Gear fatigue crack prognosis using embedded

model, gear dynamic model and fracture mechanics’, Mechanical Systems and

signal Processing, Vol.19, No.4, pp.836–846.

[82]. Liao H.T., Elsayed E.A., and Chan L.Y. (2006), ‘Maintenance of continuously

monitored degrading systems’, European Journal of Operational Research,

Vol.175, No.2, pp.821–835.

[83]. Lim J., and Park D.H. (1995), ‘Trend change in mean residual life’, IEEE

Transactions on Reliability, Vol.44, No.2, pp.291–296.

[84]. Liang F., Xu M., and Shun Q. (2000), ‘Competitive supervised learning

algorithms in machine condition monitoring’, International Journal of

COMADEM, Vol.3, No.1, pp.39–46.

[85]. Liu E., and Du Zhang (2002), ‘Diagnosis of component failures in the Space

Shuttle main engines using Bayesian belief networks: a feasibility study’,

107

Proceedings of the 14th IEEE International Conference on Tools with Artificial

Intelligence, Boston, pp.181–188.

[86]. Marksberry P.W., and Jawahir I.S. (2008), ‘A comprehensive tool-wear/tool-

life performance model in the evaluation of NDM (near dry machining) for

sustainable manufacturing’, International Journal of Machine Tools and

Manufacture, Vol.48, No.7-8, pp.878– 886.

[87]. Maria Anityasari and Hartmut Kaebernick (2008), ‘A concept of reliability

evaluation for reuse and remanufacturing’, International Journal of Sustainable

Manufacturing, Vol.1, No.1, pp.3–17.

[88]. Maity K.P., Swain P.K. (2008), ‘An experimental investigation of hot

machining to predict tool life’, Journal of materials processing technology,

Vol.198, No.3, pp.344– 349.

[89]. Ming-Yi You and Guang Meng, (2011), ‘Updated proportional hazards model

for equipment residual life prediction’, International Journal of Quality and

Reliability Management, Vol.28, No.7, pp.781–795.

[90]. Mazhar M.I., Kara S., and Kaebernick H. (2007), ‘Remaining life estimation

of used components in consumer products: Life cycle data analysis by Weibull

and artificial neural networks’, Journal of operations Management, Vol.25,

No.6, pp.1184–1193.

[91]. Maria Del Carmen Carnero Moya (2007), ‘Model for the Selection of

Predictive Maintenance Techniques’, Information Systems and Operational

Research, Vol.45, No.2, pp.83–94.

[92]. Marek Balazinski, Ernest Czogala, Krzysztof Jemielniak and Jacek Leski

(2002), ‘Tool condition monitoring using artificial intelligence methods’,

Engineering Applications of Artificial Intelligence, Vol.15, No.1, pp.73–80.

[93]. Maropoulos P.G., and Alamin B. (1996), ‘Integrated tool life prediction and

management for an intelligent tool selection system’, Journal of Materials

Processing Technology, Vol.61, No.1–2, pp.225–230.

[94]. Mangun D., Thurston D.L. (2002), ‘Incorporating component reuse,

108

remanufacture, and recycle into product portfolio design’, IEEE Transactions on

Engineering Management, Vol.49, No.4, pp.479–490.

[95]. Ming-Yi You, and Guang Meng (2011 a), ‘Updated proportional hazards

model for equipment residual life prediction’, International Journal of Quality

& Reliability Management, Vol.28, No.7, pp.781–795.

[96]. Ming-Yi You, and Guang Meng, (2011 b), ‘A generalized similarity measure

for similarity-based residual life prediction’, Journal of Process Mechanical

Engineering, Vol.225, No.3, pp.151–160.

[97]. Morgan I., Honghai Liu., Tormos B., and Sala A. (2010), ‘Detection and

Diagnosis of Incipient Faults in Heavy-Duty Diesel Engines’, IEEE

Transactions on Industrial Electronics, , Vol.57, No.10, pp.3522–3532.

[98]. Macin V., Tormos B., Sala A., and Ramirez J. ( 2006), ‘Fuzzy logic-based

expert system for diesel engine oil analysis diagnosis’, Non-Destructive Testing

and Condition Monitoring, Vol.48, No.8, pp.462–469.

[99]. Mathew S., Rodgers P., Eveloy V., Vichare N., and Pecht M. (2006), ‘A

Methodology for Assessing the Remaining Life of Electronic Products’,

International Journal of Performability Engineering , Vol.2, No.4, pp.383–395.

[100]. Marjan Golob and Boris Tovornik (2008), ‘Input–output modelling with

decomposed neuro-fuzzy ARX model’, Neurocomputing, Vol.71, No.4–6,

pp.875–884.

[101]. Matías J.M., Rivas T., Martín J.E., and Taboada J. (2008), ‘A machine

learning methodology for the analysis of workplace accidents’, International

Journal of Computer Mathematics , Vol.85, No.3–4, pp.559–578.

[102]. Nagi Gebraeel, Mark Lawley, Liu .R and Vijay Prameswaran. (2004),

‘Residual life predictions from vibration based degradation signals: A neural

network approach’, IEEE transactions on industrial electronics, Vol.51, No.3,

pp.694–700.

[103]. Nagpal G.R. (2002), ‘Tool Engineering and Design’, Khanna Publishers.

109

[104]. Nguyen T., and Nadipuram Prasad (2000), ‘Fuzzy modeling and control –

selected works of M.Sageno’, CRC Press, New York, pp.17–145.

[105]. Nancy Diaz, Seungchun Choi, Moneer Helu, Yifen Chen,Stephen Jayanathan

and Yusuke Yasui (2010), ‘Machine Tool Design and Operation Strategies for

Green Manufacturing’, Proceedings of the 4th CIRP International Conference

on High performance Cutting, pp.1–6. Springer.

[106]. Nancy Diaz., Ecena redelsheimer and david Dornfeld (2011), ‘Energy

consumption for characterization and reduction strategies for milling machine

tools use’, Globalized solutions for sustainability in manufacturing, pp.263–267,

Springer.

[107]. Om Prakash Yadav, Nanua Singh, Ratna Babu Chinnam and Parveen S.Goel

(2003), ‘A fuzzy logic based approach to reliability improvement estimation

during product development, Reliability Engineering and System Safety, Vol.80,

No.1, pp.63–74.

[108]. Orchard M.E., and Vachtsevanos G.J. (2007), ‘A particle filtering-based

framework for real-time fault diagnosis and failure prognosis in a turbine

engine’, Proceedings of the Mediterranean Conference on Control and

Automation, pp.1– 6.

[109]. Otilia Elena Dragomir, Rafael Gouriveau, Nourredine Zerhouni (2008),

‘Adaptive Neuro-Fuzzy Inference System for midterm prognostic error

stabilization’, International Journal of Computers, Communications & Control,

Vol.3, pp.271–276.

[110]. Pijnenburg M. (1991), ‘Additive hazards models in repairable systems

reliability’, Reliability Engineering and System Safety, Vol.31, No.3, pp.369–

390.

[111]. Phillip J Ross. (2005), Taguchi Techniques for quality Engineering, Tata

McGraw Hill.

110

[112]. Ping Yi Chao and Yeong Dong Hwang (1997), ‘An improvement neural

network model for the prediction of cutting tool life’, Journal of Intelligent

Manufacturing, Vol.8, No.2, pp.107–115.

[113]. Ping He, Guang Fu Liu, and Dan Zhou (2012), ‘A Decision-Making Model

of Energy-Saving Design and a Case Study’, Journal of Applied Mechanics and

Materials, Vol.130–134, pp. 1586–1589.

[114]. Parkinson G.H.J., Thompson G. (2003), ‘Analysing and taxonomy of

remanufacturing industry practice’, Proceedings of the I MECH E Part B

Journal of Engineering Manufacture, Vol.217, No.3, pp.275–280.

[115]. Paya B.A., Esat I.I., and Badi M.N. (1997), ‘Artificial neural network based

fault diagnostics of rotating machinery using wavelet transforms as a

preprocessor’, Mechanical Systems and Signal Processing, Vol.11, No.5,

pp.751–765.

[116]. Przekop L.A and Kerr S(2004), ‘Life cycle tools for future product

sustainability’, Proceedings of the IEEE International symposium on Electronics

and the environment, pp.23-26.

[117]. Rajesh Kumar Bhushan (2013), ‘Optimization of cutting parameters for

minimizing power consumption and maximizing tool life during machining of

Al alloy SiC particle composites’, Journal of Cleaner production, Vol.39,

pp.242–254.

[118]. Rommert Dekker (1996), ‘Applications of maintenance optimization models:

a review and analysis’, Reliability Engineering & System Safety, Vol.51, No.3,

pp.229–240.

[119]. Sahin Y.(2009), ‘Comparison of tool life between ceramic and cubic boron

nitride (CBN) cutting tools when machining hardened steels’, Journal of

Materials Processing Technology, Vol.209, No.7, pp.3478–3489.

[120]. Salat R., and Osowski S. (2004), ‘Accurate fault location in the power

transmission line using support vector machine approach, IEEE Transactions

on Power Systems, Vol.19, No.2, pp.879–886.

111

[121]. Satchidanananda Mishra, Michale Pect, Ted Smith, Ian McNee and Roger

Harris (2002), ‘Remaining life prediction of electronic products using life

consumption monitoring approach’, European Microelectronics Packing and

Interconnection Symposium, Cracow, Poland, pp.136–142.

[122]. Siddiqui M.M., and Caglar M. (1994), ‘Residual Lifetime Distribution and its

Applications’, Microelectronics Reliability, Vol.34, No.2, pp.211–227.

[123]. Sidda Reddy B., Suresh Kumar J., Vijaya Kumar Reddy and Padmanabhan

G. (2009), ‘Application of soft computing for the prediction of warpage of

plastic injection molded parts’, Journal of Engineering science and Technology

review, Vol.2, No.1, pp.52–62.

[124]. Saha B., Goebel K., Poll S., and Christophersen J. (2009), ‘Prognostics

methods for battery health monitoring using a Bayesian framewor’, IEEE

Transactions on Instrumentation and measurement, Vol.58, No.2, pp.291–296.

[125]. Schneider E.L, Kindlein Jr. W., Souza S., and Malfatti C.F. (2009),

‘Assessment and reuse of secondary batteries cells’, Journal of Power Sources,

Vol.189, No.2, pp.1264–1269.

[126]. Srinivas J., Rama Kotaiah. (2005), ‘Tool wear monitoring with indirect

methods’, Manufacturing Technology Today, Vol.4, pp.7–9.

[127]. Shaw M.C. (1991), ‘Metal Cutting Princlpes’, Oxford, NY.

[128]. Sheng P., Srinivasan M., and Kobayashi S. (1995), ‘Multi-objective process

planning in environmentally conscious manufacturing: a feature-based

approach’, CIRP Annals - Manufacturing Technology, Vol.44, No.1, pp.433–

437.

[129]. Sivarao (2009), ‘Mamdani fuzzy inference system modeling to predict

surface roughnes in laser machining’, International Journal of Intelligent

Information Technology Application, Vol.2, No.1, pp.12–18.

112

[130]. Sikorska J.Z., Hodkiewicz M., and Ma L. (2011), ‘Prognostic modeling

options for Remaining useful life estimation by industry’, Mechanical Systems

and Signal Processing, Vol.25, No.5, pp.1803–1836.

[131]. Sathiyasekar K., Thyagarajah K., and Krishnan A. (2011), ‘Nero fuzzy based

predict the insulation quality of high voltage rotating machine’, Expert Systems

with Applications, Vol.38, No.1, pp.1066–1072.

[132]. Sivarao, Castillo and Taufik (2009), ‘Machining quality predictions:

comparative analysis of neural net work and fuzzy logic’, International Journal

of Electrical & Computer Sciences, Vol.9, No.9, pp.451–456.

[133]. Smola A.J., and Scholkopf B. (1998), ‘A tutorial on support vector

regression’, NeuroCOLT2 Technical report series, NC2-TR-1998-030, ESPRIT

Working Group in Neural and Computational Learning.

[134]. Smita B Brunnermeier and Mark A Cohen (2003), ‘Determinants of

environmental innovation in US manufacturing industries’, Journal of

Environmental Economics and Management, Vol.45, No.2, pp. 278–293.

[135]. Samanta B., and Al-balushi K.R. (2003), ‘Artificial neural network based

fault diagnostics of rolling element bearings using time-domain features’,

Mechanical Systems and Signal Processing, Vol.17, No.2, pp.317–328.

[136]. Satish B., and Sarma N.D.R. (2005), ‘A fuzzy BP approach for diagnosis and

prognosis of bearing faults in induction motors’, Proceedings of the IEEE

Conference on Power Engineering Society General Meeting, Vol.3, pp.2291–

2294.

[137]. Shunfeng Cheng and Pecht M. (2009), ‘A fusion prognostics method for

remaining useful life prediction of electronic products’, Proceedings of the IEEE

International Conference on Automation Science and Engineering, pp.102–107.

[138]. Shangguan D (2004), ‘ Environmental leadership in electronic

manufacturing: lead-free and beyond’, Proceedings of the IEEE international

symposium on Electronics and the Environment, pp 33-39.

[139]. Tan X.C., Liu F., Cao H.J., and Zhang H. (2002), ‘A decision-making

framework model of cutting fluid selection for green manufacturing and a case

113

study’, Journal of Materials Processing Technology, Vol.129, No.1-3, pp.467–

470.

[140]. Tan X.C., Liu F., and Li C. (2008), ‘A decision-making framework model of

cutting tool selection for green manufacturing and its application’, Journal of

Advanced Manufacturing Systems, Vol.7, No.2, pp.257–260.

[141]. Tang L.C., Lu Y., and Chew E.P. (1999), ‘Mean residual life of lifetime

distributions’, IEEE Transactions on Reliability, Vol.48, No.1, pp.73–78.

[142]. Takata S., Kimura F., Van Houten F.J.A.M., Westkämper E., Shpitalni M.,

Ceglarek D., and Jay Lee. (2004), ‘Maintenance: Changing Role in Life Cycle

Management’, CIRP Annals - Manufacturing Technology, Vol.53, No.2,

pp.643–655.

[143]. Thukaram D., Khincha H.P., and Vijaynarasimha H.P. (2005), ‘Artificial

neural network and support machine approach for locating faults in radial

distributionsystems’, IEEE Transactions on Power delivery, Vol.20, No.2,

pp.710–721.

[144]. Tugrul Ozel and Yigit Karpat (2005), ‘Predictive modeling of surface

roughness and tool wear in hard turning using regression and neural networks’,

International Journal of Machine Tools and Manufacture, Vol.45, No.4–5,

pp.467–479.

[145]. Tugrul Ozel, Yigit Karpat, Luís Figueira and Paulo Davim J. (2007),

‘Modelling of surface finish and tool flank wear in turning of AISI D2 steel with

ceramic wiper inserts’, Journal of Materials Processing Technology, Vol.189,

No.1–3, pp.192–198.

[146]. Ulas Caydas and Sami Ekici (2012), ‘Support vector machines models for

surface roughness prediction in CNC turning of AISI 304 austenitic stainless

steel’, Journal of intelligent manufacturing, Vol.23, No.3, pp.639–650.

[147]. Uluyol O., Kyusung Kim and Nwadiogbu E.O. (2006), ‘Synergistic use of

soft computing technologies for fault detection in gas turbine engines’, IEEE

Transactions on Systems, Man, and Cybernetics, Part C: Applications and

Reviews, Vol.36, No.4, pp.476–484.

114

[148]. Vachtsevanos G., Lewis F.L., Roemer M., Hess A., and Wu B. (2006),

Intelligent fault diagnosis and prognosis for engineering systems, Wiley, NY.

[149]. Van Tung Tran, Bo-Suk Yang, Myung-Suck Oh and Andy Chit Chiow Tan,

(2008), ‘Machine condition prognosis based on regression trees and one-step-

ahead prediction’, Mechanical Systems and Signal Processing, Vol.22, No.5,

pp.1179–1193.

[150]. Vapnik V. (1995), The nature of statistical learning theory, Berlin

Heidelberg, New York, Springer-Verlag.

[151]. Vapnik V., Golowich S., and Smola A. (1997), ‘Support vector method for

function approximation regression estimation and signal processing’,

Proceedings of the 1996 Neural Information Processing Systems Conference

NIPS 1996, CO, USA, MIT Press, Cambridge, pp.281–287.

[152]. Virk S.M., Muhammad A., and Martinez-Enriquez A.M. (2008), ‘Fault

Prediction Using Artificial Neural Network and Fuzzy Logic’, Proceedings of

the Seventh Mexican International Conference on Artificial Intelligence,

Atizapan de Zaragoza, pp.149–54.

[153]. Wang W., and Zhang W. (2005), ‘A model to predict the residual life of

aircraft engines based upon oil analysis data’, Naval Research Logistics,Vol.52,

No.3, pp.276–284.

[154]. Wen-Hsien Ho, Jinn-Tsong Tsai, Bor-Tsuen Lin and Jyh-Horng Chou

(2009), ‘

Adaptive network-based fuzzy inference system for prediction of

surface roughness in end milling process using hybrid Taguchi-genetic learning

algorithm’, Expert Systems with Applications, Vol.36, No.2, pp. 3216–3222.

[155]. Wang Zhaoqiang , Hu Changhua , Wang Wenbin , Si Xiaosheng , Zhou

Zhijie (2012), ‘An off-online fuzzy modelling method for fault prognosis with

an application’, Proceedings of the IEEE Conference on Prognostics and System

Health Management (PHM), Beijing, China, pp.1–7.

[156]. Witten I.H., and Frank E. (2000), ‘Data Mining: Practical Machine Learning

Tools and Techniques with Java Implementations’, Morgan Kaufmann

Publishers, SanFrancisco, CA.

115

[157]. Wei- Chiang Hong and Ping-Feng pai (2006), ‘Predicting engine reliability

by support vector Machines’, International Journal of Advanced Manufacturing

Technology, Vol.28, No.1–2, pp.154–161.

[158]. Wenjia Xu and Wenbin Wang (2012), ‘An adaptive gamma process based

model for residual useful life prediction’, Proceedings of the IEEE Conference

on Prognostics and System Health Management (PHM), Bejing, pp.1–4.

[159]. Wahyu Caesarendra, Achmad Widodo and Bo-Suk Yang (2010),

‘Application of relevance vector machine and logistic regression for machine

degradation assessment’, Mechanical Systems and Signal Processing, Vol.24,

No.4, pp.1161–1171.

[160]. Wahyu Caesarendra, Achmad Widodo and Bo-Suk Yang (2011),

‘Combination of probability approach and support vector machine towards

machine health prognostics’, Probabilistic Engineering Mechanics, Vol.26,

No.2, pp.165–173.

[161]. Xiao-Sheng Si, Chang-Hua Hu, Jian-Bo Yang, and Zhi-Jie Zhou (2011a), ‘A

New Prediction Model Based on Belief Rule Base for System's Behavior

Prediction’, IEEE Transactions on Fuzzy Systems, Vol.19, No.4, pp.636–651.

[162]. Xiao Sheng Si, Wenbin Wang, Chang_Hua Hu and Dong-Hua Zhou (2011

b), ‘Remaining useful life estimation- A review on the statistical data driven

approaches’, European Journal of Operational Research, Vol.213, No.1, pp.1–

14.

[163]. Xiaoh Wang (2009), ‘Intelligent modeling and predicting surface roughness

in end milling’, Proceedings of the fifth IEEE international conference on

Natural Computation, Tianjin, pp. 521–525.

[164]. Xiaojun Zhou, Lifeng Xi and Jay Lee (2007), ‘Reliability-centered predictive

maintenance scheduling for a continuously monitored system subject to

degradation’, Reliability Engineering and System Safety, Vol.92, No.4, pp.530 –

534.

[165]. Xu K, Xie M, Tang L.C., and Ho S.L. (2003), ‘Application of neural

networks in forecasting engine systems reliability’, Applied Soft Computing,

Vol.2, No.4, pp.255– 268.

116

[166]. Yanbin Du, Huajun Cao, and Xian Chen, and Bentao Wang (2013), ‘Reuse-

oriented redesign method of used products based on axiomatic design theory and

QFD’, Journal of Cleaner production, Vol.39, pp.79–86.

[167]. Yinhui Ao and George Qiao (2010), ‘Prognostics for drilling process with

wavelet packet decomposition’, International Journal of Advanced

Manufacturing Technology, Vol.50, No.4, pp.47–52.

[168]. Yang Xiang, Chenwen Ye and Deborah Ann Stacey (2002), ‘Application of

Bayesian Networks to Shopping Assistance’, Advances in Artificial Intelligence,

Lecture Notes in Computer Science, Vol.2338, pp.344–348.

[169]. Yong-min Yang, Zhe-xue Ge and Yong-cheng Xu (2008), ‘Fault Diagnosis

of Complex Systems Based on Multi-sensor and Multi-domain Knowledge

Information Fusion’, Proceedings of the IEEE International Conference on

Networking, Sensing and Control, pp.1065–1069.

[170]. Yasmine rosunally, Stoyan stoyanov, Chris bailey, Peter mason, Sheelagh

campbell, George monger and Iian bell (2010), ‘Bayesian Networks for

Predicting Remaining Life’, International Journal of Performability

Engineering, Vol.6, No.5, pp.499–512.

[171]. Yue Jiao, Shuting Lei, Pei Z.J., and Lee. E.S. (2004), ‘Fuzzy adaptive

networks in machining process modeling: surface roughness prediction for

turning operations’, International Journal of Machine Tools and Manufacture,

Vol.44, No.15, pp.1643–1651.

[172]. Zuperl Uros, Cus Franc and Kiker Edi (2009), ‘Adaptive network based

inference system for estimation of flank wear in end-milling’, Journal of

Materials Processing Technology, Vol.209, No.3, pp.1504–1511.

[173]. Zhao F, Chen J and Xu W (2008), ‘Condition prediction based on wavelet

packet transform and least squares support vector machine methods’,

Proceedings of the IMech E, Part E:J.Process Mechanical Engineering,

Vol.223, pp.71–79.

[174]. Zhang J. H. R. (2004), ‘A new algorithm of improving fault location based

on SVM’, Proceedings of the Eighth IEE International Conference on

Development in Power system Protection, Vol.1, pp.204–207.

117

[175]. Zhigang Tian (2009), ‘An artificial neural network method for remaining

useful life prediction of equipment subject to condition monitoring’,

International Journal of ManufacturingTechnology,

www.springerlink.com/openurl.asp/DOI 10.1007/s10845-009-0356-9.

[176]. Zhigang Tian, Lorna Wong and Nima Safaei (2010), ‘A neural network

approach for remaining useful life prediction utilizing both failure and

suspension histories’, Mechanical systems and Signal Processing, Vol.24, No.5,

pp.1542–1555.

[177]. Zhu J.Y., and Deshmukh A. (2003), ‘Application of Bayesian decision

networks to life cycle engineering in Green design and manufacturing’,

Engineering Applications of Artificial Intelligence, Vol.16, No.2, pp.91–103.