# Improving Deep Learning Anomaly Diagnostics with a Physics-Based Simulation Model
## 1 Introduction  
In manufacturing systems, identifying potential problems early on is vital for safety and cost-effectiveness. Detecting issues promptly not only ensures a secure operational environment but also helps prevent financial losses by addressing concerns before they escalate [1]. This is particularly critical in high-energy processes, where machine breakages or anomalies in machinery may pose a threat to the overall process operation and to the safety of the operating personnel. Physics-based simulation models and various machine learning tools are used to detect anomalies by analyzing data and recognizing unusual patterns or behaviors that could signal potential risks for failures. However, the identification success depends heavily on the availability of representative training data [2] together with the inclusion of good-quality data to avoid inaccurate analytics and unreliable decision-making [3]. Poor-quality or inconsistent data can lead to inaccurate results, which can lead to false-negative or false-positive occurrences in the classification process. Without proper data, it is difficult for machine learning algorithms to distinguish between normal and abnormal dispersion, whereas creating understandable representations of the data makes it easier to extract useful information when constructing classifiers or other predictive models [4]. Physics-based simulation models are used across industries to create a baseline for a machine system's normal operation and to observe deviations from normal behavior by simulating the system in a computer environment. By understanding the baseline performance of the machine system, deviations from that baseline can be quickly identified and addressed. Also, physics-based solutions may provide additional information in the absence of real data, whereas the absence of breakdown samples prompts a need for further investigation into the development of effective methods to detect anomalies in production data, rather than solely focusing on predicting fault events [5]. [PERSON] and [PERSON] focused on generating background and signal events with their simulation model for the training of machine learning classifiers for distinguishing anomalies from normal operation data [6]. A model-based method for real-time anomaly detection that contained a dynamic regression model and adaptive anomaly threshold was trialed by [7] for the forecast of electricity generation needs, load frequency control, and demand forecast in the power industry. In the aviation industry, [PERSON] and [PERSON] [8] presented model-based methods for anomaly detection for use with aircraft engines by comparing the outputs observed from an engine with the outputs predicted by their physics-based simulation model. Their research results indicated that making improvements to the trim-point information based on the simulation-model results also improved the effectiveness of anomaly detection. Various machine learning (ML) techniques are also used to detect anomalies in machine systems. In industrial applications, the ML model's ability to recall past events is essential because it helps to identify patterns and trends in data sets that can be used to make predictions of future events or outcomes. A well-known artificial neural network (ANN) used in deep learning is called the recurrent neural network (RNN) and is widely in use with time series data [9; 10]. RNNs can remember information over long sequences of inputs and can process data one element at a time while retaining information about the previous elements. One application of an RNN is Long Short-Term Memory (LSTM), which can be used in regression as well as in a classification type of problem-solving [11; 12]. Supervised ML classification performance can be evaluated by comparing the true positives, false positives, true negatives, and false negatives between the labeled true states and the predicted states.