## 1 Introduction
As two-dimensional CNN networks require image data for processing, when CNNs are used for sensor fault classification, the one-dimensional sensor signals are converted to images using methods like Short-Time Fourier Transform (STFT) [22], scalograms [23], and wavelet transforms [24]. As sensor recordings are sequential in nature, recurrent neural networks (RNNs) are more suitable for them as these deep networks have the ability to capture long-term sequential dependencies and can handle variable sequence lengths. Although machine learning (ML) algorithms such as SVM, RF, KNN, and others have been suggested for sensor fault analysis, they come with certain limitations. ML algorithms require careful feature extraction from raw sensor data, which can be both challenging and time-consuming. Additionally, ML algorithms do not inherently capture the temporal dependencies in time series data, which is crucial in sensor fault detection, where understanding the relationship between data points as they change during fault occurrences is essential. In real-world scenarios, faulty instances in sensor signals are expected to occur  
Figure 1: Sensor fault types. less frequently, leading to an imbalance between faulty and non-faulty classes in practical datasets. Unfortunately, ML models often struggle with imbalanced data. In contrast, deep learning (DL) models have the capability to automatically extract features, and models such as RNN, LSTM, and TCN can capture the temporal dependencies in time series data. These models are also suitable for multivariate time series scenarios. However, deep learning models require considerable computational resources, and power consumption is a significant concern in real-world deployments. Both ML and DL approaches have their strengths and weaknesses in developing a methodology for sensor fault detection and classification. Given the nature of the data, DL models with recurrent structures are well suited for capturing temporal dependencies. However, for simpler implementations, ML models are more desirable. In this work, we aim to combine both approaches to leverage their strengths while keeping the overall approach less complex by maintaining a simple DL model architecture and considering a small number of features necessary for an ML model. It is observed in the literature that when recurrent models are used for fault or abnormality detection in time series, multivariate data are typically considered. However, for fault detection and classification in individual sensors deployed in real environments, a univariate approach is often more practical. Multi-step prediction is a relatively newer concept for time-series-related tasks. While several multi-step prediction models have been proposed for other applications, their use in sensor fault analysis has not been widely explored. Therefore, in this work, we investigate how this approach can be applied to sensor fault detection and classification. The major contributions of this paper are as follows. * A Long Short-Term Memory autoencoder (LSTM AE)-based univariate multi-step-ahead forecasting approach is proposed for sensor fault detection and classification. This method uses only normal sensor data for training. * A feature extraction step is integrated into the prediction model to enable the detection of multiple types of faults. Additionally, three influential statistical features are reported that better represent bias, drift, and stuck faults in sensors. * Results obtained from two different datasets are presented to demonstrate the functionality of the proposed approach. This sensor fault-detection and classification scheme can be integrated into real-world systems where sensing activity is critical, such as in IoT networks. The inclusion of sensor fault analysis is essential for ensuring data reliability.