System for real-time monitoring of mechanical vibrations and for fault prediction in rotating machines

Abstract

A system for real-time monitoring of mechanical vibrations and for fault prediction in rotating machinery, the system comprising: a housing designed for mounting on a rotating machine component; a plurality of vibration sensors mounted inside or on the housing and positioned relative to predefined positions of the rotating machine, each vibration sensor comprising at least one multi-axis accelerometer and an associated signal conditioning circuit with amplification and filtering elements configured to generate conditioned analog vibration signals; a data acquisition unit electrically connected to the plurality of vibration sensors and comprising an analog-to-digital conversion circuit configured to sample the conditioned analog vibration signals at a predetermined sampling rate and generate synchronized digital vibration data streams;a processing unit operationally linked to the data acquisition unit and comprising at least one microprocessor configured to perform real-time signal processing operations, including time-domain statistical analyses, frequency-domain transformations, and time-frequency decomposition to extract diagnostic vibration characteristics; a predictive computing unit integrated into or coupled with the processing unit and configured to analyze the diagnostic vibration characteristics using adaptive modeling to generate failure predictions and remaining service life estimates for one or more components of the rotating machine; and a communication interface configured to transmit the failure prediction outputs and diagnostic vibration characteristics to an external monitoring or control system.

Description

AREA OF INVENTION The present invention relates generally to systems for the condition monitoring of machines and, in particular, to a hardware-integrated system for real-time vibration measurement and fault prediction in rotating machines. The invention falls within the field of industrial diagnostics and prognostics and comprises instruments and embedded electronic devices for acquiring, processing, and transmitting vibration signals generated by mechanical components such as shafts, bearings, gears, turbines, motors, and compressors. The invention relates in particular to electromechanical sensor arrangements, signal conditioning circuits, hardware for high-speed data acquisition, and processing circuits for performing cross-domain vibration analysis to detect mechanical anomalies and estimate component wear.The described system is applicable in various industries, including manufacturing, energy production, oil and gas processing, and transportation, where continuous monitoring of rotating equipment is crucial for operational safety, reliability, and efficiency. BACKGROUND OF THE INVENTION Rotating machinery such as turbines, compressors, motors, pumps, and gearboxes are fundamental components in industry, manufacturing, and power generation. These machines are constantly subjected to dynamic loads, rotational stresses, temperature fluctuations, and mechanical wear, leading to the progressive degradation of components such as bearings, shafts, couplings, and gears. Conventional maintenance strategies, including scheduled inspections and reactive repairs, often fail to detect incipient failures, resulting in unexpected breakdowns, downtime, and increased maintenance costs. Existing vibration monitoring systems typically rely on periodic data acquisition or simple, threshold-based alerts. They lack the capability for high-resolution, real-time analysis and predictive failure modeling.Furthermore, such systems often cannot correlate multidimensional vibration patterns with evolving fault conditions under different operating conditions. Therefore, there is a need for an advanced system that continuously monitors mechanical vibrations in real time, extracts meaningful diagnostic features, and predicts fault conditions with high accuracy to enable proactive maintenance and improved system reliability. Mechanical vibration monitoring has long been considered a fundamental method for assessing the condition and operational safety of rotating machinery such as turbines, pumps, compressors, and electric motors. Vibration behavior reflects the dynamic interaction of rotating components, and deviations from normal vibration patterns indicate faults such as imbalance, shaft misalignment, bearing wear, gear damage, and loosening. Traditional vibration monitoring methods rely on periodic inspections using portable vibration analyzers, where measurements are taken at discrete intervals and analyzed offline. While these methods provide useful snapshots of machine condition, they have limited temporal resolution and cannot detect transient or rapidly evolving fault conditions.Therefore, defects may go undetected between inspection intervals, which can lead to unexpected breakdowns and costly downtime. To overcome these limitations, continuous condition monitoring systems have been developed, comprising permanently installed vibration sensors and central monitoring units. These systems typically use accelerometers mounted on key machine components and transmit the data to a control unit for analysis. However, many of these conventional systems rely on threshold-based alarm mechanisms, where predefined vibration amplitude limits trigger alarms. Such approaches are inherently limited because they do not account for variations in operating conditions such as load, speed, and environmental influences. Consequently, threshold-based systems frequently generate false alarms or fail to detect faults in their early stages, as long as the predefined limits have not yet been exceeded. Advanced vibration analysis techniques, including frequency domain analysis using fast Fourier transform (FFT), have been introduced to identify characteristic fault frequencies of specific machine components. While FFT-based systems improve diagnostic capabilities, they are often implemented in centralized architectures that require significant computing resources and high data transmission bandwidth. This poses a challenge for real-time operation, particularly in distributed industrial environments where multiple machines operate simultaneously. Furthermore, frequency domain methods can struggle to detect non-stationary or transient vibration signals, which are important indicators of early-stage fault development. Newer solutions utilize time-frequency analysis methods such as wavelet transforms and short-time Fourier transforms to capture transient features. While these methods improve sensitivity to evolving errors, they increase computational complexity and require careful parameter setting. Furthermore, many existing implementations process data in batch mode rather than in real time, limiting their ability to predict errors immediately. Machine learning methods for predictive maintenance have also become established, utilizing historical vibration data to train models capable of recognizing fault patterns. While these systems offer improved predictive accuracy, they are heavily reliant on the availability of large, high-quality datasets with appropriate labels, which are often difficult to obtain in industrial environments. Furthermore, such models may lack generalizability across different machine types and operating conditions, reducing reliability in heterogeneous environments. Another drawback is that many machine learning methods operate as black-box models, limiting the interpretation of diagnostic results and potentially hindering their application in safety-critical systems. Furthermore, existing systems often lack tight integration of the components sensors, data acquisition, data processing, and predictive analytics. This fragmented architecture leads to latency, synchronization problems, and increased system complexity. Communication delays and data loss in distributed monitoring systems further impair performance and reliability. Conventional systems are also often bulky, require extensive cabling, and are difficult to integrate into existing machinery without significant modifications. Despite advances in vibration monitoring technology, current solutions still face limitations in terms of real-time processing, adaptability to dynamic operating conditions, prediction accuracy, and system integration. These shortcomings underscore the need for a more coherent, intelligent vibration monitoring system that operates in real time and enables precise fault predictions with minimal latency and increased operational stability. SUMMARY OF THE INVENTION The present invention provides a system for real-time monitoring of mechanical vibrations and for fault prediction in rotating machines. The system comprises a structurally integrated sensor and processing unit that continuously acquires vibration data from critical machine components, performs signal analysis in various areas, and generates predictive indicators for mechanical faults. The system includes multiple vibration sensors mounted at predefined positions on the rotating machine structure. Each sensor comprises microelectromechanical accelerometers and a signal conditioning circuit for acquiring high-frequency vibration signals along multiple axes. A data acquisition unit is coupled to the sensors and digitizes analog vibration signals with high sampling resolution. It synchronizes the data streams across multiple measurement positions.A processing unit performs real-time signal processing operations, including time-domain analysis, frequency-domain transformation, and time-frequency decomposition, to extract characteristic features of mechanical anomalies. The system also includes a predictive computing unit that applies adaptive modeling techniques such as pattern recognition and correlation of historical trends to estimate the failure progression and remaining service life of machine components. The system is designed as a device that can be structurally integrated into rotating machinery or externally attached to it, enabling continuous operation without interrupting machine functionality. The present invention primarily relates to a system for the real-time monitoring of mechanical vibrations and for fault prediction in rotating machinery. This system enables the continuous, high-resolution acquisition of vibration data at multiple points within the machine structure, thus facilitating the early detection of mechanical anomalies before they lead to critical failures. A further objective of the invention is a structurally integrated device for acquiring multi-axis vibration signals and for synchronized data acquisition to ensure a precise representation of the machine dynamics under various operating conditions. Furthermore, the invention relates to a system for real-time signal processing, including time, frequency, and time-frequency analysis, to extract diagnostic features that indicate specific fault conditions such as imbalance, misalignment, bearing defects, and gear wear. A further objective of the invention is to provide a predictive function within the system that analyzes historical and real-time vibration data to estimate the failure progression and determine the remaining service life of critical machine components. This enables predictive maintenance strategies. A further objective of the invention is a system that minimizes false alarms and improves diagnostic accuracy through adaptive analysis methods that take into account load, speed, and environmental influences. Another objective of the invention is a compact and robust device that can be easily integrated into or retrofitted to existing rotating machinery without significant structural modifications or operational interruptions. A further objective of the invention is to provide a system with high data acquisition and processing speed in order to reduce the latency between signal acquisition and fault prediction, thereby ensuring timely decision-making and response. A further objective of the invention is to provide a communication interface for transmitting diagnostic and predictive information to external monitoring systems or control centers for remote monitoring and maintenance planning. A further objective of the invention is to increase operational reliability, reduce unplanned downtime, and extend the service life of rotating machinery through continuous condition monitoring and intelligent fault prediction.Furthermore, the invention aims to provide a scalable and adaptable solution that can be used for various types of rotating equipment and industrial environments, ensuring consistent performance and accuracy. BRIEF DESCRIPTION OF THE DRAWING These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. 1 shows a block diagram of a system for real-time monitoring of mechanical vibrations and for fault prediction in rotating machines. Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. DETAILED DESCRIPTION OF THE INVENTION To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention. It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof. References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment. The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components. Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting. Embodiments of the present disclosure are described in detail below with reference to the attached drawing. Fig. 1 shows a block diagram of a system for real-time monitoring of mechanical vibrations and for fault prediction in rotating machines. The system comprises: a housing 102 for mounting on a component of the rotating machine; several vibration sensors 104, which are mounted in or on the housing and arranged relative to predefined positions of the rotating machine. Each vibration sensor includes at least one multi-axis accelerometer and an associated signal conditioning circuit with amplification and filtering elements for generating conditioned analog vibration signals; a data acquisition unit 106, which is electrically connected to the vibration sensors and includes an analog-to-digital conversion circuit for sampling the conditioned analog vibration signals at a predefined sampling rate and for generating synchronized digital vibration data streams;a processing unit 108, which is connected to the data acquisition unit and includes at least one microprocessor for performing real-time signal processing operations such as statistical analysis in the time domain, frequency domain transformation, and time-frequency decomposition for extracting diagnostic vibration characteristics; a predictive computing unit 110, which is integrated into or coupled with the processing unit and configured to analyze the diagnostic vibration characteristics using adaptive modeling to generate fault prediction outputs and remaining service life estimates for one or more components of the rotating machine; and a communication interface 112, which is configured to transmit the fault prediction outputs and the diagnostic vibration characteristics to an external monitoring or control system. In one embodiment, each vibration sensor unit 104 further comprises a three-axis accelerometer configured to measure vibration components in radial, axial and tangential directions relative to the rotating machine, wherein the signal conditioning circuit includes an anti-aliasing filter and a noise reduction stage configured to improve the signal quality prior to analog-to-digital conversion. In one embodiment, the data acquisition unit 106 further comprises a synchronization circuit configured to coordinate the sampling times across the plurality of vibration sensor units in order to enable coherent multi-point vibration analysis and phase correlation. In one embodiment, the analog-to-digital conversion circuit of the data acquisition unit 106 is configured to operate with a variable sampling frequency that adapts to the rotational speed or operating state of the rotating machine. In one embodiment, the processing unit 108 is further configured to calculate statistical parameters for each vibration data stream, including the root mean square, peak amplitude, crest factor, skewness and kurtosis, in order to detect deviations from normal operating conditions. In one embodiment, the processing unit 108 is further configured to perform a frequency domain analysis using a fast Fourier transform algorithm to identify characteristic frequency components corresponding to bearing defects, shaft misalignment, rotor imbalance or gear engagement anomalies. In one embodiment, the processing unit 108 is further configured to perform a time-frequency decomposition using a short-time Fourier transform or wavelet transform in order to detect transient vibration events and non-stationary signal characteristics. In one embodiment, the predictive computing unit 110 comprises a storage unit that stores historical vibration data and a computing circuit configured to correlate real-time diagnostic vibration characteristics with stored historical data to determine wear trends and fault progression patterns. In one embodiment, the predictive computing unit 110 is further configured to generate a remaining service life parameter based on trend extrapolation and threshold-based failure criteria associated with one or more components of the rotating machine. In one embodiment, the communication interface 112 comprises a wireless transmission circuit or a wired communication port configured to transmit diagnostic and predictive data to a remote monitoring station or higher-level control system using industrial communication protocols. The system is implemented as a physically realized device in which each functional element corresponds to a defined hardware structure configured to perform specific operations. The housing is a rigid enclosure for direct mechanical mounting on rotating machinery, ensuring positional stability and protection from environmental influences. The numerous vibration sensors consist of microelectromechanical accelerometers in semiconductor devices, each coupled to discrete analog circuits. These include amplifiers, resistive and capacitive filter networks, and shielding components for signal conditioning. The data acquisition unit incorporates dedicated analog-to-digital converters, clock generators, and synchronization circuits comprised of hardware timing components to ensure synchronous sampling across all channels.The processing unit consists of a microprocessor or digital signal processor integrated on a semiconductor substrate, along with associated memory circuits and bus architectures for performing signal transformation operations. The predictive computing unit includes hardware memory arrays and arithmetic circuits for iterative calculations and trend analysis. The communication interface comprises physical transmission components such as transceivers, modulation circuits, and wired interfaces that enable data transmission via electrical or electromagnetic signals. Each of these elements is interconnected via conductive traces and integrated into the device, creating an integrated electromechanical system suitable for continuous operation in industrial environments. The present invention describes a system for real-time monitoring of mechanical vibrations and for predicting fault conditions in rotating machines. The system consists of a rigid housing for mounting on machine structures such as bearing housings, shaft supports, or gearbox housings. The housing encloses several sensor units, a data acquisition unit, a processing unit, and communication circuits. Each sensor unit includes at least one triaxial accelerometer for detecting vibration signals along orthogonal axes corresponding to the radial, axial, and tangential directions of the rotating machine. The sensor units also include signal conditioning circuits with amplifier stages, anti-aliasing filters, and noise reduction components to ensure precise detection of the vibration signals under various operating conditions. The data acquisition unit is electrically coupled to the sensor units and includes an analog-to-digital conversion circuit that captures vibration signals at high sampling rates, typically in the range of several kilohertz to megahertz, depending on the machine's operating frequency. The data acquisition unit also incorporates a synchronization circuit that synchronizes the data streams from multiple sensor units, thus enabling a coherent multi-point vibration analysis. The digitized vibration data is transmitted to the processing unit via high-speed communication channels within the device. The processing unit consists of a microprocessor or digital signal processor configured to execute real-time vibration analysis algorithms. It performs a time-domain analysis, including the calculation of statistical parameters such as RMS, peak amplitude, crest factor, and kurtosis, to detect deviations from normal operation. Furthermore, the processing unit performs a frequency-domain analysis using transformation techniques such as the fast Fourier transform to identify characteristic frequency components associated with specific fault types, such as bearing defects, imbalance, misalignment, and gear damage. Additionally, the processing unit applies time-frequency decomposition techniques, such as the short-time Fourier transform or wavelet-based analysis, to capture transient vibration events and evolving fault signatures. The system also includes a predictive computing unit (PCU) that is operationally integrated into the processing unit. This PCU analyzes extracted features over time and generates predictive models for the failure process. It uses adaptive learning mechanisms that correlate real-time vibration patterns with historical data to estimate wear trends and predict the onset of failure. The PCU also calculates a remaining service life parameter, which indicates the expected operating time before a component reaches a failure threshold. The device also includes a communication interface for transmitting processed data, diagnostic results, and predictive indicators to an external monitoring system or control center. The interface supports wired and wireless transmission protocols, enabling remote monitoring and integration into higher-level control systems. The device can also include a local display interface for real-time visualization of vibration levels and fault indicators. During operation, the sensor units continuously record vibration signals from the rotating machinery and transmit processed signals to the data acquisition unit. This unit digitizes and synchronizes the signals before forwarding them to the processing unit. The processing unit extracts relevant characteristics and identifies anomalies in real time, while the predictive computing unit analyzes the failure history and generates forecasts. The system thus enables the early detection of mechanical failures, reduces unplanned downtime, and increases the reliability and operational efficiency of rotating machinery. The structural integration of sensor, data acquisition, processing, and forecasting components in a single device ensures compact deployment and minimal disruption to existing machine configurations. The system is adaptable to various types of rotating machinery and operating environments, and its real-time analysis capability represents a significant advancement over conventional vibration monitoring methods. The present invention relates to a system in the form of a structurally integrated device for real-time monitoring of mechanical vibrations and for fault prediction in rotating machines. The system comprises coordinated hardware components such as sensor units, data acquisition circuits, processing circuits, predictive calculation circuits, and communication interfaces, arranged in a compact housing that can be attached to the machine structure. During operation, the multiple vibration sensor units, each comprising at least one multi-axis accelerometer, continuously generate analog vibration signals corresponding to the dynamic motion in radial, axial, and tangential directions. These signals are first processed by amplification stages and anti-aliasing filters to remove high-frequency noise and avoid spectral distortions before digitization.The processed signals are then forwarded to the data acquisition unit, where analog-to-digital conversion takes place at a sampling rate based on the rotational speed and the expected vibration frequency range of the machine. The data acquisition unit also synchronizes all sensor channels using a common clock to ensure the phase alignment of the acquired vibration data streams. The digitized and synchronized vibration data is then transferred to the processing unit, where a series of real-time analysis operations are performed. First, the processing unit conducts a time-domain analysis by segmenting the incoming data into sliding windows of predefined duration and calculating statistical parameters such as RMS values, peak amplitudes, crest factors, skewness, and kurtosis. These parameters provide immediate indicators of anomalous vibration energy levels and impulse events associated with incipient faults. Following the time-domain analysis, the processing unit applies a frequency-domain transformation using a fast Fourier transform to decompose the vibration data into spectral components.The resulting frequency spectrum is examined to identify characteristic peaks that correspond to known fault frequencies, including those associated with inner and outer ring bearing defects, rolling element defects, shaft alignment harmonics, and gear engagement frequencies. To address non-stationary and transient vibration phenomena, the processing unit performs a time-frequency decomposition using short-time Fourier transforms or wavelet-based methods. This process decomposes the vibration signal into localized time-frequency representations, enabling the detection of transient pulses, modulation patterns, and evolving fault signatures that might not be visible in stationary spectral analysis. The processing unit aggregates the features extracted from time-frequency and time-frequency analyses into a multidimensional feature vector representing the machine's current operating state. The predictive computing unit receives the feature vector and processes it together with stored historical vibration data from an associated memory. It implements an adaptive modeling algorithm that establishes correlations between real-time features and historical wear patterns. The algorithm uses trend analysis techniques, tracking feature evolution over successive time intervals to identify progressive deviations from the initial state. Statistical regression and pattern recognition are applied to estimate the rate of change of critical features, thus enabling the prediction of future machine conditions. Furthermore, the predictive computing unit calculates a parameter for the remaining service life by comparing predicted characteristic values ​​with predefined failure thresholds of specific components. This involves extrapolating characteristic trends using time series forecasting methods and determining the point in time at which the extrapolated values ​​reach the failure criteria. The predictive computing unit continuously updates its model parameters based on incoming data, thus adapting to changes in operating conditions such as load fluctuations, speed variations, and environmental influences. The communication interface enables the transfer of processed data, extracted features, and forecast results to external systems for monitoring and decision-making. It supports both wired and wireless communication protocols, thus ensuring compatibility with industrial control networks. In certain configurations, the system can also include an integrated local display or an alarm mechanism that provides immediate feedback on the machine's condition. The system's algorithmic flow is implemented in a pipeline architecture within the processing unit. This enables the simultaneous execution of data acquisition, feature extraction, and predictive analysis with minimal latency. Buffer management techniques ensure a continuous, lossless data flow, and interrupt-driven processing mechanisms efficiently handle high-frequency data streams. The integration of synchronized sensors, real-time signal processing, and adaptive predictive computing in a single device allows the system to detect faults early, predict failure states with high accuracy, and support proactive maintenance strategies. This significantly improves the reliability and operational efficiency of rotating machinery. The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims. The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A system for real-time monitoring of mechanical vibrations and for fault prediction in rotating machinery. 102 Structural housing 104 Multiple vibration sensor units 106 Data acquisition unit 108 Processing unit 110 Predictive calculation unit 112 Communication interface

Claims

A system for real-time monitoring of mechanical vibrations and for fault prediction in rotating machinery, the system comprising: a housing designed for mounting on a rotating machine component; a plurality of vibration sensors mounted inside or on the housing and positioned relative to predefined positions of the rotating machine, each vibration sensor comprising at least one multi-axis accelerometer and an associated signal conditioning circuit with amplification and filtering elements configured to generate conditioned analog vibration signals; a data acquisition unit electrically connected to the plurality of vibration sensors and comprising an analog-to-digital conversion circuit configured to sample the conditioned analog vibration signals at a predetermined sampling rate and generate synchronized digital vibration data streams;a processing unit operationally linked to the data acquisition unit and comprising at least one microprocessor configured to perform real-time signal processing operations, including time-domain statistical analyses, frequency-domain transformations, and time-frequency decomposition to extract diagnostic vibration characteristics; a predictive computing unit integrated into or coupled with the processing unit and configured to analyze the diagnostic vibration characteristics using adaptive modeling to generate failure predictions and remaining service life estimates for one or more components of the rotating machine; and a communication interface configured to transmit the failure prediction outputs and diagnostic vibration characteristics to an external monitoring or control system. System according to claim 1, wherein each vibration sensor unit further comprises a three-axis accelerometer configured to measure vibration components in radial, axial and tangential directions relative to the rotating machine, and wherein the signal conditioning circuit comprises an anti-aliasing filter and a noise reduction stage configured to improve signal quality prior to analog-to-digital conversion. System according to claim 1, wherein the data acquisition unit further comprises a synchronization circuit configured to coordinate the sampling times across the plurality of vibration sensors in order to enable coherent multi-point vibration analysis and phase correlation. System according to claim 1, wherein the analog-to-digital conversion circuit of the data acquisition unit is configured to operate with a variable sampling frequency that adapts to the rotational speed or operating state of the rotating machine. System according to claim 1, wherein the processing unit is further configured to calculate statistical parameters such as RMS value, peak amplitude, crest factor, skewness and kurtosis for each vibration data stream in order to detect deviations from the basic operating conditions. System according to claim 1, wherein the processing unit is further configured to perform a frequency domain analysis using a fast Fourier transform algorithm to identify characteristic frequency components corresponding to bearing defects, shaft misalignment, rotor imbalance or gear engagement anomalies. System according to claim 1, wherein the processing unit is further configured to perform a time-frequency decomposition using a short-time Fourier transform or wavelet transform to detect transient vibration events and non-stationary signal characteristics. System according to claim 1, wherein the predictive computing unit comprises a storage unit for storing historical vibration data and a computing circuit configured to correlate real-time diagnostic vibration features with stored historical data to determine degradation trends and fault progression patterns. System according to claim 1, wherein the predictive computing unit is further configured to generate a remaining service life parameter based on trend extrapolation and threshold-based failure criteria associated with one or more components of the rotating machine. System according to claim 1, wherein the communication interface comprises a wireless transmission circuit or a wired communication port configured to transmit diagnostic and predictive data to a remote monitoring station or a higher-level control system using industrial communication protocols.

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