Accurate estimation of railway track geometry degradation and reliable prediction of remaining useful life (RUL) are essential for cost-effective infrastructure management. This paper presents a self-contained Kalman filtering framework that integrates unsupervised tamping detection and Settling-aware post-tamping state management, jointly addressing state estimation, short-term forecasting, and RUL prediction for longitudinal level and twist parameters using only historical onboard monitoring (OBM) and measurement train (MT) data, without requiring traffic load, environmental, or maintenance metadata. The framework comprises four integrated stages: (i) source-specific outlier detection exploiting the distinct noise characteristics of OBM and MT instruments; (ii) unsupervised tamping detection through adaptive jump analysis on monthly representative values; (iii) a damped Kalman filter with adaptive noise modelling and spatial fusion of neighboring positions, augmented by a Settling-aware state management mechanism that detects the rapid post-tamping consolidation phase and injects physically informed velocity priors to prevent filter lag; and (iv) a RUL prediction module that propagates the final Kalman state forward under damped dynamics until the predicted geometry violates the Alert Limit (AL) or Immediate Action Limit (IAL) defined by EN 13848-5. The complete pipeline is evaluated on 50 consecutive track positions spanning a 50-metre segment of Sudostbahn line 870 (Switzerland), using 6 MT and 33 OBM observations per position collected over five years (2016-2021). Results demonstrate accurate estimation through both degradation and maintenance phases, with six-month forecast confidence bands. The multi-position RUL analysis classifies 98% of positions as safe beyond a 2-year horizon and identifies the remaining positions with finite RUL values, enabling spatially targeted maintenance prioritization.

The proposed evaluated Capability Maturity Model (CMM) for long-term management of Zero Defect Manufacturing (ZDM) implementations, ZDM-CMM, is intended as a tool for organizations initiating or advancing their ZDM initiatives. This ZDM-CMM offers guidance on key perspectives crucial for successful ZDM development, deployment, and potential objectives for measuring maturity and progress over time. The evaluation of the ZDM-CMM highlights use in balancing conflicting objectives such as speed, cost, and quality, with sensitivity analysis recommended to determine their relative importance. While additional objectives were suggested, the current model provides a foundational framework to be refined with further experience. The CMM, designed primarily for tracking maturity rather than comparing implementations, emphasizes the significance of considering various perspectives early in ZDM project planning to ensure comprehensive management. Although not yet widely adopted, the companies that participated in the evaluation expressed interest in using the CMM for visualization of progress and holistic assessment of ZDM efforts. Further, the ZDM-CMM is expected to provide significant insights for project planning, potentially saving time, resources, and ensuring thorough consideration of critical ZDM aspects. 

The paper addresses, based on a qualitative study comprising five companies, requirements for interoperability concerning Zero Defect Manufacturing (ZDM) in an Industry4.0 context. The results outline a strong link between well working interoperability and achieving the benefits from ZDM efforts. Further, the results also outline the need for getting out the right data from production processes and keep high data quality supported by management attention and a clear vision. The results are aligned with existing literature and studies but bring up more details on requirements on evolving interoperability for ZDM in terms of data quality and how the analytic output can be used for decisions based on the reliability and confidence levels. In addition, the increased need for cybersecurity within ZDM is highlighted where also cybersecurity requires interoperability to function well over time. 

Simulation of thermal dynamics in city-scale district energy grids often becomes computationally prohibitive for long simulation runs. Most current model order reduction methods offer limited interpretability with regards to the nonreduced system and are not in general applicable for varying flow rates, multiple producers, or changing flow directions. This article presents a graph perspective on modeling of district energy networks. Based on spectral graph theory, a novel method for model order reduction of district energy systems is proposed. The method approximates the solution of an optimization problem, minimizing the coefficients of the local truncation error for the advection equation. Furthermore, a method for calculating the intracluster temperature distribution is presented. It is shown that the method can be used to reduce the thermal dynamic model of a city-scale energy grid, resulting in a coarser temporal and spatial resolution, with a significant decrease in simulation time. The relative root-mean-square error (rRMSE) was 2.5% for the temperature in the evaluation scenario, comparing the reduced-order system with the nonreduced system at the instances of the coarser time step.

This article proposes an online solution to address the problem of closed-loop system identification using multiple recursive least squares estimation protocols. Some control systems cannot be analysed in an open-loop form for stability reasons or the requirement for online control system operation. So, it is necessary to identify plant dynamics and controller parameters based on input–output data from the feedback structure. The presented method identifies real-time parameters of plant dynamics and controller parameters by utilising a series of recursive least square estimation algorithms that estimate open-loop data from noisy input–output data measured from the closed-loop feedback structure. The proposed method can effectively identify abrupt variations in both the controller parameters and plant dynamics. This capability makes it valuable for deployment as a supervisory component, enabling the detection of any faults that may arise in operating systems. Mathematical formulations and theorems are developed, and two numerical case studies are presented to examine the feasibility and performance of the presented closed-loop system identification protocol.

The supply temperature in district heating systems has traditionally been controlled using feedforward – a robust and well-validated approach for district heating networks with few producers and relatively high supply temperatures. The transition towards lower temperature district heating networks allows for efficient reuse of excess heat from, e.g., industrial processes and data centers. Excess heat is often intermittent, cannot always be assumed to be possible to control with a centralized controller, and can cause temperature disturbances in the grid. Closing the loop using PID control is challenging due to the process’s time-varying nature and long time delays. Model Predictive Control (MPC) suffers from a higher complexity, long computational times, and the need for a well-validated and maintained centralized model. The paper suggests a semi-decentralized approach using the Smith predictor with an event-driven assignment of active controllers and sensors. A reduced order model based on a more comprehensive state space model is derived and used for gain scheduling and input–output pairing using the normalized relative gain array. The focus is on temperature disturbance rejection, and appropriate tuning rules and controller structures are suggested. Simulation results show that the proposed control structure can handle various types of temperature disturbances, even in the presence of model estimation errors.

This work introduces a method for closed-loop system identification using frequency analysis, employing Empirical Transfer Function Estimation (ETFE). By integrating optimization within a Monte Carlo framework, it enhances the precision of ETFE, resulting in minimized frequency response errors compared to actual system data. Leveraging controller information in an offline model fitting scheme, it achieves optimal realization of process dynamics. The method is evaluated on a data center rack-level cooling system, showing Bode magnitude plots of actual and estimated closed-loop and open-loop dynamics, with confidence intervals demonstrating algorithm consistency. Numerical evaluations confirm the feasibility and potential of the approach to improve offline closed-loop system identification performance in the frequency domain, beneficial for analysis and design. There will not be a comparative study for the introduced approach.

This paper presents an adaptive control allocation technique for over-actuated systems. The online actuator selection algorithm is used to select the best group of actuators. Also, a multi-objective cost function is utilized for the allocation unit. The virtual and actual control signals in the control allocation methodologies are linked by the effectiveness matrix. In many practical systems, the elements of the effectiveness matrix may vary due to changing operating conditions, nonlinearities, ageing, disturbances and faults. Hence, an online algorithm for estimation of the entries of the effectiveness matrix is presented in this paper. Estimation of the effectiveness matrix will be used for the proposed adaptive actuator selection strategy, employing the Actuator Effectiveness Index (AEI). The AEI is calculated for all the actuators, and the best group of actuators will be subsequently selected. Finally, simulation results are used to show the effectiveness of the proposed methodology.

This review aims at assessing the opportunities and challenges of creating and using digital twins for process industrial systems over their life-cycle in the context of estimation and control. The scope is, therefore, to provide a survey on mechanisms to generate models for process industrial systems using machine learning (purely data-driven) and automated equation-based modeling. In particular, we consider learning, validation, and updating of large-scale (i.e., plant-wide or plant-stage but not component-wide) equation-based process models. These aspects are discussed in relation to typical application cases for the digital twins creating value for users both on the operational and planning level for process industrial systems. These application cases are also connected to the needed technologies and the maturity of those as given by the state of the art. Combining all aspects, a way forward to enable the automatic generation and updating of digital twins is proposed, outlining the required research and development activities. The paper is the outcome of the research project AutoTwin-PRE funded by Strategic Innovation Program PiiA within the Swedish Innovation Agency VINNOVA and the academic version of an industry report prior published by PiiA.