**1. Introduction**

In an industrial scenario, three-phase induction machines have several applications due to their reliability, availability, and cost-effectiveness. Unexpected faults in these machines

could lead to unexpected breakdowns, losses at industrial production, or have catastrophic consequences. In this context, rolling element bearings are responsible for more than 40% of induction machine faults [1]. Rolling bearings are critical mechanical components, which allow relative movement between systems, supporting radial and thrust load. Bearing faults could be associated to contamination, corrosion, inadequate lubrication, installation problems, and misalignment or overloading [2]. In general, a fault affects only one bearing component—inner race, outer race, cage, or ball; as the fault evolves, it spreads to other components; moreover, these faults could be described based on fault mechanism, location, or on a combination of these [3, 4].

Maintenance of electrical machines is an activity of the utmost importance. Moreover, considering a scenario of cost reduction and production efficiency, the development of an effective maintenance program has been gaining more attention and several tools have been implemented to support and encourage best practices. In this sense, advanced methods for data acquisition and processing have been developed in order to allow an effective machine condition monitoring and early fault detection and identification, avoiding unexpected breakdowns and even catastrophic failures, especially for critical systems. Whenever possible, condition monitoring should be done non-invasively and without interrupting machine operation [5–7].

Over the years, the concept of maintenance became more comprehensive, reducing fault occurrence and increasing industrial system availability. Besides, requirements of reliability, safety, and criticality were associated with the system or equipment under analysis. Maintenance strategies or schemes can be classified as corrective (run-to-break), preventive (time-based) and predictive (condition-based maintenance) [8]. Corrective maintenance is only performed after an occurrence of a fault and therefore involves unexpected breakdowns, high costs, changes in the production chain, and in addition, it could lead to catastrophic events. Preventive maintenance and interventions occur based onto a scheduled maintenance plan or based on the equipment mean time between failures. Although it is more effective than corrective maintenance, by preventing most failures, unexpected failure may still occur. Additionally, the process cost is still high, especially, the costs associated with labor, inventory, and even with unnecessary replacement of equipment or components [8, 9].

On the other hand, predictive maintenance analyses the equipment condition so that a possible fault can still be identified at an early stage. Predictive maintenance aims to identify a machine anomaly so that it does not result in a fault. Such maintenance involves advanced technique of monitoring, processing, and signal analysis, that are generally performed noninvasively and, in many cases, in real time. In case of induction machines, these techniques can be developed based on vibration, temperature, acoustic emission, or electrical current signal monitoring [9]. It should be noted that the monitoring of such signals or parameters, in order to verify the operating condition of a machine, is called condition monitoring. In fact, condition monitoring aims to not only observe machine current operational condition, but also to predict machine future condition, keeping it under a systematic analysis during the machine's remaining life [8]. In this sense, from a systematic machine condition monitoring, a fault condition can be detected and identified, such that, a diagnosis procedure can be established, whereby properly investigating the fault symptoms and prognosis [10].

In general, the machine's monitored signals are the result of a combination of different sources, which can vary according to machine environmental and operational conditions, monitoring and acquisition systems, among others. Besides, some faults, such as bearing faults, can produce a signal modulation, which give rise to other signals (sidebands and harmonics) [8]. Thus, a great challenge in machine diagnosis consists of separating and identifying these sources. In case of bearing fault detection in induction machines, the motor current signature analysis (MCSA) has emerged as one of the leading condition monitoring techniques. This approach is an advantageous alternative (or complementary) to condition monitoring based on vibration analysis. Many machines already have current monitoring for control or protection purposes, not requiring the installation of other types of sensor, therefore, this approach can be considered non-invasive and cost-effective. Over the years, the spectral estimation using techniques based on Fourier transform has been widely applied for analysis of the stator current [11–13]. This analysis methodology considers the use of stationary signals, that is, considers that the machine is operating at constant speed and load. On the other hand, advanced techniques take into account the nonstationary signals [11].

In this context, this work consists of applying a methodology for analyzing the electrical signature for the diagnosis of point defects in bearings induction motor, based on spectral kurtosis and squared envelope spectrum analysis, in order to increase the fault detection capability even in an incipient stage [14].
