Structural Health Monitoring (SHM) is increasingly applied in civil engineering. One of its primary purposes is detecting and assessing changes in structure conditions to reduce potential maintenance downtime. Recent advancements, especially in sensor technology, facilitate data measurements, collection, and process automation, leading to large data streams. We propose a function-on-function regression framework for modeling the sensor data and adjusting for confounder-induced variation. Our approach is particularly suited for long-term monitoring when several months or years of training data are available. It combines highly flexible yet interpretable semi-parametric modeling with functional principal component analysis and uses the corresponding out-of-sample phase-II scores for monitoring. The method proposed can also be described as a combination of an `input-output' and an `output-only' method.

Self-starting control charts as introduced by Quesenberry and Hawkins are of practical interest to monitor processes for which we do not know parameters based on historical data. We will show a case study with the Royal Dutch Navy from the national PrimaVera project to illustrate this.

Since self-starting control charts update the estimates of the unknown parameters at every observation, one might think that even if the observations are independent, the resulting control chart statistics are no longer independent, which would cause technical problems in assessing the performance of the control chart procedure. However, there are claims in the literature that surprisingly, independence of the statistics of self-starting control charts is preserved. We will indicate that there are problems with the correctness of these proofs. We will show how to adapt one of the existing proofs so that we not only get a clear, correct proof for the one-dimensional case, but also a proof for the more general case of regression control charts.

This is based on joint work with bachelor student Gijs Pennings, while the case study was performed by master student Esmée Stijns together with Wieder Tiddens and Tiedo Tinga from the Dutch Royal Navy.

The online quality monitoring of a process with low volume data is a very challenging task and the attention is most often placed in detecting when some of the underline (unknown) process parameter(s) experience a persistent shift. Self-starting methods, both in the frequentist and the Bayesian domain aim to offer a solution. Adopting the latter perspective, we propose a general closed-form Bayesian scheme, whose application in regular practice is straightforward. The testing procedure is build on a memory-based control chart that relies on the cumulative ratios of sequentially updated predictive distributions. The derivation of control chart's decision-making threshold, based on false alarm tolerance, along with closed form conjugate analysis, accompany the testing. The theoretic framework can accommodate any likelihood from the regular exponential family, while the appropriate prior setting allows the use of different sources of information, when available. An extensive simulation study evaluates the performance against competitors and examines the robustness to different prior settings and model type misspecifications, while continuous and discrete real datasets, illustrate its implementation.

Generative AI applications such as ChatGPT, GitHub Copilot, Bard, Midjourney, and others have created worldwide buzz and excitement due to their ease of use, broad utility, and perceived capabilities. This talk will introduce two projects both first attempts to understand the impact of ChatGPT on analytics.

In the first part, I will introduce ChatSQC, an innovative chatbot system that combines the power of OpenAI’s Large Language Models (LLM) with a specific knowledge base in Statistical Quality Control (SQC). Our research focuses on enhancing LLMs using specific SQC references, shedding light on how data preprocessing parameters and LLM selection impact the quality of generated responses.

In the second part, I will share ongoing work focused on defining quality metrics to evaluate Generative AI’s analytics capabilities. Currently, Generative AI systems are evaluated mainly in designing and training the LLM models that generate output in various forms depending on the user’s request. The models are not, however, universally evaluated based on the quality of the output in terms of the output’s fitness for use by the user. We therefore define user oriented quality metrics and evaluate, from a user perspective, the LLMs generated output in a variety of analytics tasks.