# IoT System for Detecting the Condition of Rotating Machines Based on Acoustic Signals
## 1 Introduction  
Predictive maintenance (PdM) is a well-known paradigm in machine maintenance in which the machine is observed through the available measurements and its health is estimated as well as a potential need for repair [1, 2]. It is well known that the probability of failure of all machines increases with time due to component wear [3]. In this situation, it is crucial to perform timely maintenance because the replacement of worn components can increase the lifetime of the machine significantly. One of the major areas of interest of industrial predictive maintenance is the state estimation of rotary machines [4]. Therefore, it is desirable to design a system that can assess the condition of different types of such machinery. PdM techniques for rotary machines can be generally divided into model-based and data-driven methods [2]. Model-based approaches require a priori knowledge of a system model that is often not easy to determine [2]. Nevertheless, such techniques can be very powerful and their accuracy can be improved by online estimation of model parameters. [PERSON] et al. developed a mathematical model of the tube-ball mill and used the Genetic algorithm (GA) for online model parameters' update in order to enhance condition monitoring of such system [5]. On the other hand, in the data-driven approaches, the necessaryprocess information is extracted directly from a large number of recorded signals [2]. These methods usually use some statistical signal processing method to extract useful information from measurements and a machine-learning algorithm to obtain prediction results [1]. For instance, [PERSON] et al. developed a method with PCA analysis of vibration data and the k-nearest neighbor (KNN) classifier for diagnosing the bearing defects [6]. [PERSON] et al. used Fourier transform (FFT) to extract the features and support vector machines (SVM) to diagnose bearing faults [7]. Other techniques such as partial least squares (PLS) [8], extreme learning machine (ELM) [9] and learning vector quantization (LVQ) [10] are also successfully used for predictive maintenance and fault detection purposes. With the rise of deep learning research, this area gained even more attention. Multilayer neural networks (MNN) were used by [PERSON] et al. to detect an imbalance in the quadrotor's propeller [11], while [PERSON] et al. in [12] utilizes a deep neural network (DNN) for AC motor state monitoring and tests the influence of the number of kernels on classification result. Due to their diversity, all these available solutions tend to be specialized for a specific type of rotary machines such as rolling element bearings [13], mills [14], turbines [15], and so forth. Solutions that can be adjusted to a wide range of problems are usually quite expensive, require a number of sensor elements and processors, and are often tied to specific industry manufacturers. Although vibration sensors are traditionally used in PdM systems [16] for rotary machines, the use of acoustic sensors should be considered due to their low cost and contactless acquisition. The acoustic signals are more susceptible to noise than vibration signals, but they have also proven effective in the state estimation [17; 18; 19]. This potential is additionally explored throughout this paper. On the other hand, with the development of Industry 4.0 and Industrial Internet of Things (IoT) paradigm, predictive maintenance strategies have become more popular than ever [3]. IoT architecture enables various devices with sensors to communicate with each other and/or with a remote computer system through the Internet. Therefore, data collection and analysis became much easier, which is crucial for proper predictive maintenance and implementation of intelligent PdM methods [20].