# A Review on Vibration-Based Condition Monitoring of Rotating Machinery
## 1 Introduction  
As of the past fifty years, highly technological methodologies have made it possible to monitor operating conditions, allowing for intelligent decisions about the maintenance interventions of plants or components, in any kind of industry, in order to achieve an effective maintenance. These are the well-known condition monitoring or predictive maintenance techniques, which significantly improve productivity, reliability, efficiency, and operating safety [1]. These techniques, based on measurements continuously carried out on the machinery (online condition monitoring) or performed at fixed time intervals (offline condition monitoring), aim to detect changes in the signals caused by damaged components with a clear distinction between anomalous alterations and changes caused by normal variations in the operating conditions of a system. Diagnostics are based on two spaces, the measurement space and the fault space; the mapping of the first space into the second makes it effective [2]. Regarding rotating machines--with rotor-type mechanisms, there are various industrial components in which condition monitoring research focuses on, such as rolling [3; 4] and journal bearings [5], gearboxes [6], shafts [7], blades [8], entire devices [9], wind turbines [10; 11], reciprocating machines [12], electric motors [13], pumps [14], helicopters [15; 16; 17], fans [15], cam mechanisms [18; 19], generators [20], and compressors [20]. Different diagnostic parameters (conditions) can be monitored. In an overall view, we may include vibrations, acoustic emissions, currents, flow, speed, pressure, temperature,lubricant conditions, strain, wear, rotor-to-stator rubbing, etc. Among these, vibration is the condition that is most widely and effectively used in the industry for rotary machines. As reported by [PERSON] and [PERSON] in their work in 2019 [3], vibration based condition monitoring allows detecting 90% of faults or failure in machines, since each system/device component has its own vibration signature, closely related with the machine operating conditions. Faults or damages in components generate additional dynamic forces (periodic or stochastic by nature), which generate vibrations in specific frequency ranges. The faults that can be detected through vibration-based condition monitoring techniques in rotary machines are manifold; among them, looseness, eccentricity, unbalance, blade defects, misalignment, defective bearings, damaged gears, and cracked or bent shafts are some of the most investigated phenomena. In the most general case, the method of implementation requires four main phases: (i) vibration measurement and pre-processing [21]; (ii) signal processing [22]; (iii) features extraction and selection [23]; and (iv) diagnostics [23,24]. An initial bibliographic analysis of the main review works in the literature concerning the condition monitoring of rotating machinery revealed interesting insights. Table 1 collects the analyzed documents, sorted in descending order, based on the date of publication. Although all reviews in Table 1 concern the condition monitoring of rotating machines, only some of them specifically refer to vibration analysis. Analyzing the object of the condition monitoring works, referring to a specific component or machine, can be classified as \"focused papers\". The other works that deal with methodologies at a general level can be classified within a general class. Table 1 also presents the main investigated topic for the focused papers, and for each article, indicates whether it focuses on a specific phase of the condition monitoring process or on the whole procedure. As it can be seen, in proportion, the number of works produced in the last five years represents the majority. The focused ones prevail over the general ones; among these, bearings and wind turbines are the most recurring themes. A good number of works deal with all phases of condition monitoring, and among those that refer to a single phase, features extraction and diagnostics are the most considered.