Fault detection using dynamic time warping (DTW) algorithm and discriminant analysis for swine wastewater treatment

Journal of Hazardous Materials

Volumn 185, Issue 1, 2011, Pages 262-268

  Jun, B H ^a  

^a Kangwon National University   (South Korea)

Author keywords

Discriminant analysis; Dynamic time warping; Fault detection; Sequencing batch reactor; Swine wastewater treatment

Indexed keywords

AVERAGE VALUES; DIAGNOSIS ALGORITHMS; DIAGNOSIS SYSTEMS; DISCRIMINANT FUNCTIONS; DYNAMIC TIME WARPING; DYNAMIC TIME WARPING ALGORITHMS; EFFECTIVE VOLUME; HIGH LOADING RATE; MAXIMUM VALUES; MINIMUM VALUE; MISCLASSIFICATIONS; OXIDATION-REDUCTION POTENTIALS; RAW WASTEWATERS; REACTION PHASE; SEQUENCING BATCH REACTORS; SHORT CYCLE; STANDARD DEVIATION; SUB-CYCLE; SWINE WASTEWATER;

BATCH REACTORS; DISCRIMINANT ANALYSIS; DISSOLVED OXYGEN; DYNAMIC ANALYSIS; FAULT DETECTION; RECLAMATION; WASTEWATER; WATER TREATMENT PLANTS;

WASTEWATER TREATMENT;

ALGORITHM; BIOREACTOR; DISCRIMINANT ANALYSIS; DISSOLVED OXYGEN; OXIDATION; PIG; REDUCTION; WASTEWATER; WATER TREATMENT;

AERATION; AEROBIC CAPACITY; ALGORITHM; ANOXIA; ARTICLE; DISCRIMINANT ANALYSIS; DYNAMIC TIME WARPING ALGORITHM; OXIDATION REDUCTION REACTION; SEQUENCING BATCH REACTOR; WASTE WATER MANAGEMENT; WASTE WATER TREATMENT PLANT;

AGRICULTURE; ALGORITHMS; ANIMALS; BIOREACTORS; DISCRIMINANT ANALYSIS; INDUSTRIAL WASTE; OXYGEN; SEWAGE; SWINE; TIME FACTORS;

SUIDAE;

EID: 78549235379     PISSN: 03043894     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhazmat.2010.09.027     Document Type: Article

References (18)

1. Bernet N., Beline F. Challenges and innovations on biological treatment of livestock effluents. Bioresour. Technol. 2009, 100:5431-5436.
2. Andreottola G., Foladori P., Ragazzi M. On-line control of a SBR system for nitrogen removal from industrial wastewater. Water Sci. Technol. 2001, 43:93-100.
3. Kim J.H., Chen M., Kishida N., Sudo R. Integrated real-time control strategy for nitrogen removal in swine wastewater treatment using sequencing batch reactors. Water Res. 2004, 38:3340-3348.
4. Ga C.H., Ra C.S. Real-time control of oxic phase using pH(mV)-time profile in swine wastewater treatment. J. Hazard. Mater. 2009, 172:61-67.
5. Lamrini B., Lann M.V., Benhammou A., Lakhal K. Detection of functional states by the 'LAMDA' classification technique: application to a coagulation process in drinking water process. C. R. Phys. 2005, 6:1161-1168.
6. Bellman R. Dynamic Programming 1957, Princeton University Press, Princeton, NJ.
7. Rabiner L., Juang B.H. Fundamentals of Speech Recognition 1993, Prentice Hall, Englewood Cliffs, NJ.
8. Chen K., Wu T.Y., Zhang H.J. On the use of nearest feature line for speaker identification. Pattern Recognit. Lett. 2002, 23:1735-1746.
9. Guler I., Meghdadi M. A different approach to off-line handwritten signature verification using the optimal dynamic time warping algorithm. Digit. Signal Process. 2008, 18:940-950.
10. Carlos V.P., Marcos F.Z., Juan M.P. An efficient low cost approach for on-line signature recognition based on length normalization and fractional distances. Pattern Recognit. 2009, 42:183-193.
11. Ramaker H.J., van Sprang E.N.M., Westerhuis J.A., Smilde A.K. Dynamic time warping of spectroscopic BATCH data. Anal. Chim. Acta 2003, 498:133-153.
12. Gollmer K., Posten C. Supervision of bioprocesses using a dynamic time warping algorithm. Control Eng. Pract. 1996, 4:1287-1295.
13. Pravdova V., Walczak B., Massart D.L. A comparison of two algorithms for warping of analytical signals. Anal. Chim. Acta 2002, 456:77-92.
14. Singh K.P., Malik A., Mohan D., Sinha S. Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)-a case study. Water Res. 2004, 38:3980-3992.
15. Doan X.T., Srinivasan R. Online monitoring of multi-phase batch processes using phase-based multivariate statistical process control. Comput. Chem. Eng. 2008, 32:230-243.
16. Kim B.C., Cheon S.P., Kim S.S., Woo H.J., Kim C.W. Fuzzy control optimization based on operator's action for the activated sludge process. The 1st IWA Conf. on Instrum. Control and Automation 2001.
17. Jun B.H., Kim D.H., Choi E.H., Bae H., Kim S.S., Kim C.W. Control of SBR operation for Piggery Wastewater Treatment with DO and ORP. J. Korean Soc. Water Qual. 2002, 18:545-551.
18. Ramaker H., van Sprang E.N., Westerhuis J.A., Smilde A.K. Dynamic time warping of spectroscopic BATCH data. Anal. Chim. Acta 2003, 498:133-153.