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The study of techniques for qualitative trend analysis (QTA) has been a popular approach to address challenges in fault diagnosis of engineered processes. Such challenges include the lack of reliable extrapolation of available models and lack of representative data describing previously unseen circumstances. Many of these challenges appear in biological systems even when normal operation can be assumed. It is for this reason that QTA techniques have also been proposed for the purpose of fault detection, automation, and dynamic modeling. In this work, we adopt a shape-constrained spline function method for the purpose of unknown input estimation. Thanks to data collected at laboratory-scale in a biological reactor for urine nitrification, this novel approach has been demonstrated successfully for the first time.

Anomaly detection is critical to process modeling, monitoring, and control since successful execution of these engineering tasks depends on access to validated data. Classical methods for data validation are quantitative in nature and require either accurate process knowledge, large representative data sets, or both. In contrast, a small section of the fault diagnosis literature has focused on qualitative data and model representations. The major benefit of such methods is that imprecise but reliable results can be obtained under previously unseen process conditions. This work continues with a line of work focused on qualitative trend analysis which is the qualitative approach to data series analysis. An existing method based on shape-constrained spline function fitting is expanded to deal explicitly with discontinuities and is applied here for the first time for anomaly detection. An experimental test case and a comparison with the principal component analysis method bear out the benefits of the qualitative approach to process monitoring.

The advent of affordable computing, low-cost sensor hardware, and high-speed and reliable communications have spurred ubiquitous installation of sensors in complex engineered systems. However, ensuring reliable data quality remains a challenge. Exploitation of redundancy among sensor signals can help improving the precision of measured variables, detecting the presence of gross errors, and identifying faulty sensors. The cost of sensor ownership, maintenance efforts in particular, can still be cost-prohibitive however. Maximizing the ability to assess and control data quality while minimizing the cost of ownership thus requires a careful sensor placement. To solve this challenge, we develop a generally applicable method to solve the multi-objective sensor placement problem in systems governed by linear and bilinear balance equations. Importantly, the method computes all Pareto-optimal sensor layouts with conventional computational resources and requires no information about the expected sensor quality.

In this paper a methodology for extraction of qualitative descriptions of data series is extended for proper inflection point recognition. The original method allows the extraction of qualitative information such as the sign of the first and second derivatives of the assumed signals underlying univariate noisy time series. However, it is not able to handle consecutive inflection points, i.e. inflection points which follow each other in time with no minimum or maximum in between them. To improve this method for proper assessment of inflection points, the original method is compared with three modifications of the original method. One of the alternatives based on repeated application of Witkin's stability criterion delivers better results for both identification of the qualitative descriptions and the locations in time of extrema and inflection points. Furthermore, the same modified method is shown to deliver the best fault diagnosis performance in a benchmark batch fermentation study. Furthermore, the same modified method is shown to deliver the best fault diagnosis performance in a benchmark batch fermentation study.