# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
## 1 Introduction  
The reliability of mechanical systems has become an increasingly major topic, as malfunctions are responsible for extra costs, non-operating time, and unscheduled maintenance. As far as rotating machines are concerned, it is well established that rolling bearings are one of the root causes of such failures, indeed, almost 50% of the faults occurring to electric motors is ball- or roller bearing-related [1]. As a consequence, the monitoring of such components is necessary to avoid unwanted malfunctions, leading researchers and practitioners to investigate condition monitoring techniques even more performing and accurate. When dealing with condition monitoring, a signal that contains data on the dynamic response of the structure to be monitored is acquired, and some processing technique is applied to such signals in order to detect the presence of faults, and possibly to identify them and to estimate their size. As far as the monitoring of rolling bearing is concerned, usually the acquired signal corresponds to the vibrations sensed in the proximity to the bearing by means of a transducer, i.e., an accelerometer, whereas the processing techniques that can be found in the literature are the most varied. A comprehensive review of such processing methods can be found in [2; 3; 4]. More recently, approaches based on machine learning, as those shown in [5], were applied to a vibration dataset in order to identify faulty bearings. Further investigations on such topics regard the enhancement of the fault detection in the presence of noise, as in [6; 7; 8]. Besides the exploitation of vibrations, acoustic emission (AE) has emerged as a valid solution, in particular regarding early-stage damages where the spall has not yet been formed on the surface [9] of the inner or outer ring. The cyclostationary of such a kind of signal can be even exploited toenhance the detection of bearing faults, as reported in [10]. However, the main drawback of AE is the required sampling frequency, which is typically higher than 1 MHz [11], resulting in a huge amount of sampled data and expensive acquisition hardware. In between the AE and the vibrations, the acoustic noise, i.e., up to 20 kHz, can be exploited to detect the presence of defects, as reported in [12; 13], where conventional and low-cost microphones were exploited achieving interesting results. The major benefit in the use of microphones is that no contact with the cage is required, meaning that the transducer can be placed in the most convenient location as close as possible to the bearing to be monitored. Regardless of the kind of acquired signal, it is worth noticing that the acquisition is outsourced to high-performance data acquisition board (DAQ), as in [14], and that the subsequent signal processing is performed by means of high-level software, e.g., Matlab, resulting in an expensive and complex to be integrated monitoring system. Some previous works have dealt with such issues using a Digital Signal Processor (DSP) [15] or a Field Programmable Gate Array (FPGA) [16], however they are still rather expensive solutions. Such aspects may prevent the bearing monitoring system from being successfully integrated in low-cost and wireless nodes, which can be the building blocks of more complex Wireless Sensor Networks (WSN). The design of wireless nodes for the bearing monitoring was already investigated in previous works. More precisely, the authors of [17] proposed a wireless module that collects the signal from an accelerometer and sends it to a server using a proprietary protocol. Similarly, in [18], data from different sensors are acquired, filtered and sent to a central unit to determine the causes of faults in induction motors. In [19], a machine learning approach is exploited to analyze the dataset collected by the nodes, and the number of nodes to maximize their life-time is investigated performing simulations.