Robust fault detection and diagnosis for multimode processes

Buildings are subject to faults in their heating, ventilation and air-conditioning (HVAC) systems that can lead to excessive energy wastage, poor indoor climate, equipment failures and high maintenance costs. Field studies have shown that employing fault detection, diagnosis and prognosis (FDDP) tools followed up with equipment services and corrections can help achieve up to 40% of energy savings within the HVAC system and improve indoor climate, increase equipment lifecycle and reduce maintenance costs. The increasing adoption of building automation systems (BAS), Internet of Things (IoT) and other smart technologies in recent years have allowed large amounts of data to be continuously collected from building systems. This data-rich environment, along with the surge in data analytics and machine learning tools, has made cost-effective data-driven FDDP strategies possible. Compared to purely physics-based methods, data-driven methods require less explicit knowledge of the underlying physical system, thus are often easier to develop, and can learn certain intricate relationships that exist among data. Within the reported data-driven FDDP methods, there exists a few research gaps: 1) data imputation methods that leverage mutual information from correlated measurements to defy poor data quality from BAS have not been utilized efficiently; 2) there lacks a systematic and scalable fault diagnosis framework that incorporates probabilistic temporal relationships to track fault evolution; 3) existing fault diagnosis strategies typically focus on traditional rule-based control strategies and their scalability for advanced control strategies such as Guideline 36 have not been explored yet; 4) active threats information such as cyber-attacks, are typically not incorporated in an FDDP framework; 5) fault prognosis strategies to preemptively identify gradual faults for predictive maintenance have rarely been studied. This research attempts to address the above-mentioned research gaps through the following: Data Imputation: reported data imputation methods that are suitable for handling and repairing multi-source BAS data are evaluated. Data collected from a medium-sized, mixed-use institution building situated in Stockholm, Sweden and a small commercial building simulated in a laboratory setup is used to evaluate five different data imputation methods. Results demonstrate that incorporating time-lagged cross-correlations within the k-Nearest Neighbor (kNN) method helps to significantly improve the imputation accuracy and minimize the impact of repaired data on data-driven algorithms. Dynamic Bayesian Network (DBN)-based Framework for Cyber-Physical Fault Diagnosis: a DBN framework with discretized conditional probabilities parameters to represent the temporal relationships among building measurements is developed. Both domain knowledge and machine learning methods are used to develop the DBN structure and parameter model. The developed framework is evaluated for both traditional rule-based and Guideline 36 controls using datasets from a real building, a laboratory building, and a virtual testbed. Results show that the developed DBN framework is effective in diagnosing and isolating faults in systems even with different control strategies. The framework also successfully distinguishes whether system abnormalities originate from cyber-attacks or naturally occurring physical faults. Potential future direction to improve fault isolation using modified DBN topological structure is also reported in this study. DBN-based Framework for Fault Prognosis: an extension of the DBN framework in conjunction with Robust Multivariate Temporal (RMT) variate selection is proposed for fault prognosis. The RMT variate selection is used to extract localized temporal features from high dimensional datasets to determine the best inputs for training forecasting models. The expected fault-free behavior of multiple target variates, selected using domain knowledge, is forecasted using incoming data. The prediction errors generated from the forecasting phase are used as evidences in the DBN inference to estimate future fault probabilities. Gradual faults simulated in the virtual testbed are used to evaluate the prognosis framework. Results show that the developed framework is effective in prognosing gradual faults by leveraging the trending growth on the prediction errors. The research presented in this thesis contributes to the overall objective of developing a robust and cost-effective DBN-based framework for fault diagnosis and prognosis of building HVAC systems. Potential solutions to other existing challenges of implementing data-driven FDDP strategies, such as obtaining high-quality datasets, handling and repairing missing data, establishing a baseline model for detecting abnormalities despite other disturbances such as weather and internal conditions changes, and extracting temporal features from timeseries data are also examined.

Fault detection and diagnosis for non-linear processes empowered by dynamic neural networks

An effective process monitoring system serves as an early warning system for influences affecting the chemical plant and helps plant operator to devise remedial actions to mitigate the adverse effects. However, the design of such system presents challenges such as complex cause-effect correlations, imprecise process model and novelty identifiability. In this work, a two-step fault detection and diagnosis framework is presented. This framework utilizes boundary models developed from mass and energy balances for each section of the chemical plant. The fault detection step consists of a fuzzy inference system (FIS) to analyze the balances and identify the faulty section if the balances deviate from the normal boundary. Then, multiple adaptive neuro-fuzzy inference system (ANFIS) classifiers are constructed to diagnose the exact root causes of bad performance. The combination of boundary models and FIS provides fault isolation of the faulty plant section even when novel faults have occurred. Utilization of multiple ANFIS classifiers reduces the complexity of the networks and improves the proficiency of the process monitoring system. The proposed scheme is applied on a model of a large scale industrial process.

Achieving asymptotic and concurrent fault diagnosis in hydraulic system remains a challenging endeavor due to the inherent attributes of the hidden occurrence, simultaneous manifestation, coupling, and limited sample size. To address the above issues, this paper proposes a hierarchical multi-output fault detection and diagnosis framework, namely, HMDF, based on a hierarchical learning strategy to leverage an improved convolutional neural network (CNN) and support vector machine (SVM). Both a multi-channel CNN and a multi-branch CNN are employed to extract and downscale features collected by the sensors at diverse sampling frequencies first, and then, such features are subsequently subjected to classification using SVM. The hierarchical learning strategy enables the identification of different fault states, both at the component and the intra-component level. Additionally, a modified whale optimization algorithm is also utilized to optimize the classification process of SVM. Extensive experiments are conducted to test the proposed HMDF with the hydraulic system datasets. Results show that HMDF achieves a diagnostic accuracy of up to 98.9% for the dataset, surpassing traditional methods reliant on manual extraction of time–frequency features, and it also exhibits superior classification performances with a small sample size. The HMDF is expected to offer a generalized framework for the multi-output fault detection and diagnosis in hydraulic systems and other complex components.

This study explores the application of an adaptive Gaussian Mixture Regression (GMR) framework for fault detection and diagnosis (FDD) across three distinct HVAC systems: passive chilled beams (PCB), radiant slabs (RS), and conventional Variable Air Volume (VAV) systems. This investigation addresses the growing need for robust and adaptable fault detection methods capable of maintaining high diagnostic precision across various HVAC configurations, which exhibit unique operational characteristics and fault signatures.The methodology follows three crucial steps: system modeling, feature selection, and FDD. First, the GMR model is used to predict the flow and the heating and cooling capacities of each HVAC system. Accurate prediction of these parameters is crucial, as they are important features during the FDD process. Next, a feature selection algorithm is implemented to identify the optimal features for fault diagnosis for each HVAC system. The selected features are then compared across the three systems to identify similarities and differences. Understanding these similarities is valuable for developing generalized fault detection strategies that can be adapted to multiple HVAC configurations, thereby improving the scalability and versatility of the FDD framework. The performance of the FDD model is evaluated by testing it against various fault scenarios in each HVAC system in the presence of both known and unknown faults. The evolving nature of the GMR model allows it to update its parameters and integrate new Gaussian components when encountering previously unknown states, thus maintaining high diagnostic accuracy over time.The feature selection process highlights both common and unique features across the HVAC systems. Common features include variables related to flow rates and heating/cooling capacity of the system which are critical for diagnosing faults in any HVAC system. Unique features, specific to each system's operational principles, further enhance the model's diagnostic precision. For instance, slab surface temperature is critical for radiant slabs, while supply air temperature deviation from the setpoint is more significant for the VAV system. In terms of fault prediction accuracy, the GMR model shows remarkable effectiveness across all three HVAC systems. The fault prediction accuracy was above 90% for all three systems, with the highest performance observed in the radiant slabs, followed by the passive chilled beams and the VAV system. The evolving capability of the GMR model plays a significant role in maintaining this high accuracy, as it allows the model to adapt to new fault patterns and operational changes over time.

This paper proposes a framework for quality-based fault detection and diagnosis for nonlinear batch processes with multimode operating environment. The framework seeks to address 1) the mode partition problem using a kernel fuzzy C-clustering method, and the optimal cluster number will be guaranteed by a between-within proportion index; 2) the diagnosis problem using a contribution rate method based on an improved kernel partial least squares (PLS) model, by which better detection and diagnosis performances are provided; and 3) the classification of online measurements using a hybrid kernel PLS regression and the Bayes inference theory, where the new coming measurement can be correctly assigned to its constituent mode. The whole framework is developed for batch processes, and applied to the hot strip mill rolling process. It is shown using the real industrial data that for faults affecting the thickness and flatness of the strip steel in this process, the detection and diagnosis abilities of the present methods are better compared with the existing methods.

The fault detection and diagnosis of continuous process is very important for the production safety and product quality. Owing to its no need to know much about the process mechanism and exact process model ,the data driven method, typically the principal component analysis (PCA) has attracted much attention by chemical researchers for monitoring process. PCA is powerful in fault detection ,however, it has difficulties in diagnosing fault correctly in complex process. In this paper, the multiblock principal component analysis (MBPCA) is applied for fault detection and diagnosis in continuous process, which uses the integral PCA to detect fault and block contribution and variables contribution to diagnose fault. The simulations on the Tennessee Eastman process show that the proposed method can not only detect fault quickly ,but also find the fault location exactly.

A Data-Driven Fault Detection and Diagnosis by NSGAII-t-SNE and Clustering Methods in the Chemical Process Industry

This paper addresses the problem of improving state estimation of dynamic industrial processes in real time for single, double, triple and quadruple fault detection and diagnosis purposes using multi-sensor data fusion strategy. The proposed monitoring systems track the process states to infer its operating conditions utilizing a decentralized Kalman filtering methodology based on state-vector fusion technique. The paper considers both the synchronous and asynchronous multi-sensor scenarios to explore their relevant data fusion problems. The performances of the resulting monitoring systems are investigated under the two possible cases of time-delayed measurements due to communication delay and multi-rate sensors. The state-vector data fusion technique is also adopted to integrate the individual state feature coming from the distributed extended Kalman filter (EKFs) so as to extract the necessary global detection and diagnostic information. The feasibility and effectiveness of the presented algorithms are demonstrated through simulation studies on a continuous stirred tank reactor (CSTR) benchmark problem.

Detection of incipient (slowly developing) faults is crucial in automated maintenance problems where early detection of worn equipment is required. In this paper, a general framework for model-based fault detection and diagnosis of a class of incipient faults is developed. The changes in the system dynamics due to the fault are modeled as nonlinear functions of the state and input variables, while the time profile of the failure is assumed to be exponentially developing. An automated fault diagnosis architecture using nonlinear online approximators with an adaptation scheme is designed and analyzed. A simulation example of a simple nonlinear mass-spring system is used to illustrate the results.

SummaryA fault diagnosis and compensation problem for two‐dimensional discrete time systems with time‐varying state delays is studied in this paper. The concerned two‐dimensional systems are described by the Fornasisi–Marchesini second model and are subject to unknown disturbances. First, a fault detection and diagnosis module is designed to obtain the information on sensor faults; a new fault detection and diagnosis integrated design, using the observer based on descriptor system approach, is proposed to detect and estimate the sensor faults. The integrated design can maximize the fault detection rate for a given false alarm rate. Sufficient conditions for the existence of the integrated fault detection and diagnosis design are derived in the context of norm evaluation and provided in terms of matrix inequalities. Second, a fault‐tolerant control module is proposed upon an existing output feedback controller. When the sensor fault occurs, the faulty measurement can be identified and corrected by the proposed fault detection and diagnosis module. In this case, the feedback controller can guarantee the performance of the closed‐loop system even when encountering sensor faults. Finally, the proposed method is applied to a thermal process to illustrate its effectiveness. Copyright © 2017 John Wiley & Sons, Ltd.

The problem of robust model-based diagnosis of process faults is addressed by means of artificial neural networks. Different structures and learning methods are investigated for both approaches to function approximation and pattern recognition. Main emphasis is placed upon static and dynamic neural nets that are used as predictors of nonlinear models for symptom generation. Dynamic neural networks are properly integrated into a generalized observer scheme. The goal is to achieve an adequate approximation of process outputs for each known class of system behavior. Symptoms are then evaluated by means of pattern classification. Application to a laboratory process is presented. A diagnosing subsystem is designed to detect incipient faults in the components of a three-tank system. It is implemented in real-time by using the SIMULINL/MATLAB programming environment. Experimental results regarding the diagnosis of single and multiple faults are included in a comparative study. It demonstrates the effectiveness of the suggested approach.

Fault diagnosis, including fault detection and isolation, is a critical task for computer networks. Among the various techniques used for online system-level diagnosis, we are interested in the approach based on temporal information processing. The delays of the computer networks are inevitable, and the fault localization process has to take into account bounded delays or the temporal constraints. Temporal information is fundamental in model-based diagnosis. There can be cycles or loops in a computer network, but the fault reasoning methods for such cases are seldom considered in the literature. This paper provides an analytic model based on the cyclic temporal constraint network (CTCN), which aims at the fault diagnosis of cyclic computer networks using temporal information. The goal of the proposed framework is twofold: given the network structures and the predetermined candidate fault causes, the CTCN model corresponding the computer network under test is formulated; based on the CTCN model, given the alarms sequences with timestamps, the fault diagnosis process is executed to determine the most likely fault cause(s) with its/their time interval(s) of occurrence(s). The reasoning method is dependent on time point and time distance information, with which the fault motivators (i.e., actors) and fault responders (i.e., victims) can be identified. The calculation process consists of three steps: 1) establishment of the objective function; 2) determination of the fault propagation paths; and 3) determination of the expected states with a given fault hypothesis. Finally, the proposed method is demonstrated via an application study, and the effectiveness of our proposed method is verified.

Dynamic model-based fault diagnosis for (bio)chemical batch processes

Fault Detection and Diagnosis (FDD) in complex industrial systems requires methods that can handle uncertain operating conditions, soft thresholds, evolving sensor behavior, and increasing volumes of heterogeneous data. Traditional model-based or rule-driven approaches offer interpretability but lack adaptability, while purely data-driven and Large Language Model (LLM)-based methods often struggle with consistency, traceability, and causal grounding. Dynamic Master Logic (DML) provides a causal and temporal reasoning structure with fuzzy rules that capture gradual drift, soft limits, and asynchronous sensor signals while preserving traceability and deterministic evidence propagation. Building on this foundation, this paper presents a DML-LLM hybrid architecture that integrates targeted LLM inference to interpret unstructured information such as logs, notes, or retrieved documents under controlled prompts that maintain domain constraints. The combined system integrates Bayesian updating, deterministic routing, and semantic interpretation into a unified FDD pipeline. In a semiconductor manufacturing case study, the proposed framework reduced time to detection (TTD) from 7.4 h to 1.2 h and improved the F1 score from 0.59 to 0.83 when compared with conventional Statistical Process Control (SPC) and Fault Detection and Classification (FDC) workflows. Provenance completeness increased from 18% to 96%, while engineer triage time was reduced from 72 min to 18 min per event. These results demonstrate that the hybrid framework provides a scalable and explainable approach to anomaly detection and fault diagnosis in sensor-rich industrial environments.