Fault diagnosis of an industrial plant using a Monte Carlo analysis coupled with systematic troubleshooting Irina Boiarkina a , Nick Depree a , Wei Yu a , David I. Wilson b , Brent R. Young a, * a Industrial Information and Control Centre, Chemical and Materials Engineering, University of Auckland, 2-6 Park Ave, Grafton, Auckland 1023, New Zealand b Auckland University of Technology, School of Engineering, Computing & Mathematical Sciences, Auckland 1142, New Zealand article info Article history: Received 1 August 2016 Received in revised form 2 February 2017 Accepted 25 February 2017 Available online 27 February 2017 Keywords: Fault diagnosis Monte Carlo Dairy Case study Quality Fortification abstract Efficiently troubleshooting a fortification issue at an industrial milk powder plant is a complex under- taking given the myriad of possible causes. Multiple causes, even when simple, are not easy to diagnose, however every single cause needs to be addressed in order to consistently meet product quality speci- fications. This paper uses statistical modelling in the form of Monte Carlo simulations to investigate the probable causes for unexpected excessive product variation. This approach alone, refines but does not completely solve, the production issues, so a systematic approach was required to definitively solve other root causes. This two-step fault diagnosis approach ensured that all of the differing causes proposed by plant personnel could be addressed, and sound recommendations for good manufacturing operations could be made and adopted. © 2017 Elsevier Ltd. All rights reserved. 1. Introduction Many processing industries have recently seen a shift away from maximising production with process control to a focus on quality, and process analytical technology (PAT) has come to stand for the assessment and control of product quality. The popularity of PAT was partly due to the strong FDA encouragement in the US phar- maceutical industry (FDA, 2004), but since has spread to other manufacturing industries (Munir, Yu, Young, & Wilson, 2015). The international dairy company Fonterra Co-operative Group Ltd, the world's largest fluid milk processor, has recently been looking to accelerate the development and use of PAT tools to achieve ‘real time quality’ (RTQ), combining the benefits of advanced process control (APC) with an explicit focus on quality (Hunter et al., 2012; Munir et al., 2015; Rimpil€ ainen, Kaipio, Depree, Young, & Wilson, 2015). The attractions of real time quality are obvious. If one is confi- dent that the product currently being manufactured is to specification, then savings can be made on off-line subsequent testing, while simultaneously minimising the possibility of pro- ducing significant amounts of off-spec product that must be recy- cled or rejected. However the development of appropriate tools to achieve this requires that one understands the nature of the un- derlying quality issue in order to carry out the appropriate corrective action. From an analysis of historical poor quality events, it was decided that this work would concentrate on the timely identification and subsequent correction of faults. Whilst seem- ingly simple, practical fault diagnosis on large interconnected plants is complicated. There is a natural human bias to search for a single phenomenological cause, as opposed to multiple, single failures, which is often unwarranted. Standard techniques for industrial fault diagnosis and moni- toring can be found in Gertler (1998) and Chiang, Russell, and Braatz (2001), those employing simple rule-based methods such as expert systems (Rich & Venkatasubramanian, 1987; Zahedi, Saba, Al Otaibi, & Mohd-Yusof, 2011), or dynamic process modelling (Bertanza, Pedrazzani, Manili, & Menoni, 2013), or even data driven multi-variate methods such as principal components analysis (PCA) and multi-variate data analysis (Eslamloueyan, 2011; Li, Alcala, Qin, & Zhou, 2011; Qin, 2012; Ralston, DePuy, & Graham, 2001; Singhal & Seborg, 2006). It may be prudent to distinguish between * Corresponding author. E-mail addresses: i.boiarkina@auckland.ac.nz (I. Boiarkina), n.depree@auckland. ac.nz (N. Depree), w.yu@auckland.ac.nz (W. Yu), diwilson@aut.ac.nz (D.I. Wilson), b. young@auckland.ac.nz (B.R. Young). Contents lists available at ScienceDirect Food Control journal homepage: www.elsevier.com/locate/foodcont http://dx.doi.org/10.1016/j.foodcont.2017.02.061 0956-7135/© 2017 Elsevier Ltd. All rights reserved. Food Control 78 (2017) 247e255