Reliability engineering plays a crucial role in modern industrial systems, aiming to minimize costly downtime and prevent safety hazards. The feasibility of automating this process largely depends on the available data. While sensor-level data analysis can reveal crucial insights into system health and operational states, often only event-driven log data is available due to technical or cost constraints. Although extensive research on log-based failure diagnosis has been conducted, particularly in the information technology (IT) sector, the application of these methods remains challenging in real-world industrial systems. Hence, we propose a modular and interpretable framework for log-based anomaly detection in industrial systems to address the interpretability and reliability shortcomings of previous approaches. The results obtained from a real-world production system validate the framework’s ability to support timely root cause analysis and facilitate predictive maintenance.

This paper introduces a high-level language compiler with IEC 61131–3 compliance capable of converting control function code written in Python into structured text. The Python-to-Structured Text Compiler aims to bridge the gap between modern language programming and PLC programming in industrial automation. The proposed Py2ST Compiler workflow includes parsing, transformation, code generation, and code import stages. The implementation of these phases is presented in this paper. And the compilation ability of the Py2ST Compiler is validated by a case study. Utilizing the Py2ST Compiler facilitates the development of PLC control functions within the Python environment. This capability enables the management of both IT and OT functions within a unified environment, enhancing the overall project's maintainability and reducing the development cycle.

This paper presents the design, implementation, and validation of a Control Function Library to support standardized design and deployment of control functions for automated industrial process operation. The Control Function Library establishes a standardized set of control functions for chemical production. Five categories (Time-based, Continuous, Reversible, Stopping, and Ramping control functions) are developed and encapsulated in the first version of the library. The Control Function Library enables scalability by providing a flexible framework for adding or modifying control functions to accommodate specific production requirements. This scalability allows control systems to adapt to changing requirements or environments without significant redevelopment efforts, especially for capability-based operation configuration and execution. Furthermore, the reuse of Control Function Libraries across various projects or applications minimizes development time and resource costs by avoiding the need to re-engineer processes from scratch for each new modular plant.

Microbial communities play key roles across diverse environments. Predicting their function and dynamics is a key goal of microbial ecology, but detailed microscopic descriptions of these systems can be prohibitively complex. One approach to deal with this complexity is to resort to coarser representations. Several approaches have sought to identify useful groupings of microbial species in a data-driven way. Of these, recent work has claimed some empirical success at de novo discovery of coarse representations predictive of a given function using methods as simple as a linear regression, against multiple groups of species or even a single such group (the ensemble quotient optimization (EQO) approach). Modeling community function as a linear combination of individual species' contributions appears simplistic. However, the task of identifying a predictive coarsening of an ecosystem is distinct from the task of predicting the function well, and it is conceivable that the former could be accomplished by a simpler methodology than the latter. Here, we use the resource competition framework to design a model where the "correct" grouping to be discovered is well-defined, and use synthetic data to evaluate and compare three regression-based methods, namely, two proposed previously and one we introduce. We find that regression-based methods can recover the groupings even when the function is manifestly nonlinear; that multi-group methods offer an advantage over a single-group EQO; and crucially, that simpler (linear) methods can outperform more complex ones.