Digital Signal Processing Techniques Research Articles

Fault Diagnosis of Wind Turbine Gearbox Using Vibration Scatter Plot and Visual Geometric Group Network

This study aims to develop a fault detection system designed specifically for wind turbine gearboxes. It proposes a hybrid fault diagnosis algorithm that combines scatter plot analysis with the visual geometric group (VGG) technique to identify various fault types, including gear rust, chipping, wear, and aging. To capture vibration signals, a three-axis vibration sensor was integrated with a NI-9234 DAQ card. Digital signal processing techniques were employed to actively filter out noise from the captured signals. Gaussian white noise was incorporated into the training data to enhance the noise resistance of the network model, which was then utilized for scatter plot generation. The VGG technique was subsequently applied to identify faults. The testing data were collected at two different speeds, with 1500 samples taken at each speed, totaling 3000 samples. For both training and testing, 400 samples of each fault type were employed for training, while 200 samples were allocated for testing. The test results demonstrated an overall identification accuracy of 97.7% for both the no-fault gearbox and the four-fault states, underscoring the effectiveness of the proposed methodology.

An intelligent BIM-enabled digital twin framework for real-time structural health monitoring using wireless IoT sensing, digital signal processing, and structural analysis

Structural health monitoring (SHM) of civil infrastructure is critically important due to its direct influence on public safety and economic activities. Exiting Building Information Modeling (BIM)-based SHM systems often use offline data processing techniques to analyze and visualize structural health data. Despite some of them adopting Internet of Things (IoT) to enable real-time sensor data collection, sensor data quality still remains uncertain due to a lack of sensor signal preprocessing in those existing systems. Additionally, the IoT-based SHM systems often disregard structural analysis domain knowledge, which is important for accurate and precise SHM. Therefore, there is still a need to improve existing systems and practices by enabling more efficient and reliable data collection and processing as well as providing more representative SHM data visualization in BIM. As such, this paper proposes an intelligent BIM-enabled digital twin (DT) framework that integrates wireless IoT sensing and communication, digital signal processing (DSP), and structural analysis domain knowledge. The proposed system (1) leverages IoT sensing and wireless communication to enable autonomous and real-time SHM sensor data collection and transmission, (2) applies and compares multiple DSP techniques to preprocess the sensor data/signals, and (3) innovatively embraces structural analysis expertise into structural behavior visualization in BIM by performing sensor data interpolation for enabling the visualization of structural behaviors at different locations of a structural element/component. The proposed BIM-enabled DT framework was demonstrated and tested for monitoring and visualizing the structural deformations of critical structural components using a prototyped structural frame subject to bending forces. The developed framework could be used and extended for any structural elements (such as beams, columns, trusses, slabs, arches, bracings, walls, footings, foundations, and girders, among others) and could be applied to any kind of structure. Experimental results showed that the proposed framework could effectively monitor and intuitively visualize the structural deformations under different load configurations with a high DT updating frequency of 5 Hz. The innovation of this study is reflected by integrating structural analysis expertise with IoT-enabling sensing data analytics in order to improve the representativeness of real-time structural behavior visualization in BIM and to advance the DT-based SHM systems in a faster and more adaptive direction. Ultimately, this paper contributes to the body of knowledge by developing a generic and easily extendable BIM-enabled DT framework for SHM with high sensor data quality and improved visualization to advance the existing practices of BIM-based SHM for civil infrastructure asset management.

Planning tools for next-generation DSP-based passive optical networks above 50G [Invited Tutorial

Next-generation optical access networks are evolving towards ultra-high bit rates (above 50 Gbps per wavelength) and extended fiber reach architectures. This trend will likely push the optoelectronics to their limits, thus requiring impairment compensation based on digital signal processing (DSP) techniques in the transceivers. In this paper, which is an invited follow-up of a tutorial given at ECOC 2023, we first present an overview of this evolving scenario and then propose a unified analytical model that is able to predict the performance of these new systems for both direct-detection and coherent transceiver types. We believe that this model can be useful for preliminary scalability studies of new access architectures (as it happens in international standardization bodies). Moreover, when they are deployed, it can be useful as a base for network planning tools, particularly if future transceivers will be, as expected, highly reconfigurable at the DSP level.

A Self‐Powered Smart White Cane for Improving Mobility of Visually Impaired Person Using a Triboelectric Nanogenerator

The present research offers an innovative approach to enhance the safety and mobility of people with visual impairments by using a smart white cane sensor. Herein, an aluminum electrode, Kapton film, and an inexpensive PET substrate are employed to develop an origami‐shaped multilayered triboelectric nanogenerator (TENG) device. The multilayered integration of three‐unit TENGs produces output with a high power of 51 μW at 50 MΩ, along with 155 V and 4.5 μA. Capacitor charging, low‐power electronics powering, and the integration of mechanoluminescent materials are demonstrated. The integration of mechanoluminescent materials enables efficient identification of visually impaired people in regions with poor lighting. A single‐electrode TENG is also implemented for successful surface material detection. To further enhance the sensor's capabilities, digital signal processing techniques are employed for obstacle recognition on their route. The intelligent white cane sensor effectively detects obstructions, offering the user instantaneous feedback and enabling more secure navigation in sophisticated environments. This study highlights the possibility of integrating TENG technologies to provide a complete solution for people with visual impairments. The smart white cane sensor has the potential to greatly increase the safety and independence of visually impaired people in a variety of outdoor and public spaces.

Advantages of Bistable Microwires in Digital Signal Processing.

The advantageous applications of magnetic bistable microwires have emerged during long-lasting research. They have a wide range of applications in the scientific sphere or technical practice. They can be used for various applications, including magnetic memories, biomedicine, and sensors. This manuscript is focused on the last-mentioned application of microwires-sensors-discussing various digital signal processing techniques used in practical applications. Thanks to the highly sensitive properties of microwires and their two stable states of magnetization, it is possible to perform precise measurements with less demanding digital processing. The manuscript presents four practical signal-processing methods of microwire response using three different experiments. These experiments are focused on detecting the signal in a simple environment without an external magnetic background, measuring with the external background of a ferromagnetic core, and measuring in harsh conditions with a strong magnetic background. The experiments aim to propose the best method under various conditions, emphasizing the quality and signal processing speed of the microwire signal.

Comparison of incremental encoder digital signal processing techniques for theinduction motor flux-torque vector control systems

The articlepresents the results of a study on the effectiveness of varioustechniques forsynchronous reference frame angular position numerical calculation in flux-torque vector control system of induction motor.Investigation was caried out taking into account the discrete nature of the angular speedsignal obtained using an incremental encoder. In this workfor investigation by simulation useda direct torque vector control system, which, in the presence of an ideal rotor angular speedsignal, ensures direct asymptotic field orientation, asymptotic tracking of torque-flux referencetrajectories, as well as asymptotic decouplingtorque and flux subsystems. The parameters of the inductionmotor and encoder used in the study correspond to those existing in traction electromechanical systems of city trolleybuses. It is shown that the discrete nature of the angular speedsignal, which usedin synchronous reference frameposition equation of flux-torque vector control systems, introduces field orientation errors and leads to current and torque ripples, which in a real system increase acoustic noise and can cause mechanical vibrations and resonance phenomena. An analysis of possible ways to reduce the influence of the speed signal discreteness on flux-torque control is performed, and a method for practical implementation of the synchronous reference frame angular position numerical calculationis proposed. This method allows ensuring conditions for more precisefield orientation and, by using an additional filter for the angular speedsignal, reducing the level of current and torque ripplesto negligibly small values without affecting the field orientation processes. The proposed solution can be used in the development of high dynamicflux-torque vector control systems for inductionmotors using incremental encoders, including for electric vehicles.

An Explicit Form of Ramp Function

In this paper, an analytical exact form of the ramp function is presented. This seminal function constitutes a fundamental concept of the digital signal processing theory and is also involved in many other areas of applied sciences and engineering. In particular, the ramp function is performed in a simple manner as the pointwise limit of a sequence of real and continuous functions with pointwise convergence. This limit is zero for strictly negative values of the real variable x, whereas it coincides with the independent variable x for strictly positive values of the variable x. Here, one may elucidate beforehand that the pointwise limit of a sequence of continuous functions can constitute a discontinuous function, on the condition that the convergence is not uniform. The novelty of this work, when compared to other research studies concerning analytical expressions of the ramp function, is that the proposed formula is not exhibited in terms of miscellaneous special functions, e.g., gamma function, biexponential function, or any other special functions, such as error function, hyperbolic function, orthogonal polynomials, etc. Hence, this formula may be much more practical, flexible, and useful in the computational procedures, which are inserted into digital signal processing techniques and other engineering practices.

High-speed data transmission over a microresonator frequency comb with dispersion compensation for augmented data rates and reach

Abstract Microresonator frequency comb-based high-speed data transmission provides a pathway towards augmented data capacity without increasing the number of laser sources. Their use with intensity-modulated direct detection modulation (IMDD) formats is especially pertinent in data center communications where minimizing cost, latency and complexity is paramount. This however implies that the same extent of digital signal processing techniques commonly used in coherent detection for the management of fiber impairments including chromatic dispersion are not available. With the proliferation of silicon photonics technologies in data center transceivers integrated dispersion compensation which can overcome fiber impairments would be of great merit. We demonstrate low power generation of the primary comb state in a silicon nitride microresonator and transmission of 25 Gb/s NRZ and 50 Gb/s PAM4 data over 20 km of single mode fiber. This represents the longest fiber reach demonstrated to date for the transmission of IMDD data using an integrated, microresonator frequency comb. An integrated, tunable grating device for dispersion compensation that reduces dispersion impairments after several fiber lengths generates significant improvements in the eye diagram, six orders of magnitude improvement in the bit-error rate and 14 dB improvement in the transmitter and dispersion eye closure quaternary values. Concurrently, doubling data transmission is demonstrated via polarization multiplexing a comb line and successful dispersion compensation of up to 20 km.

Analysis of Eeg Data Using Different Techniques of Digital Signal Processing

This paper explores the application of digital signal processing (DSP) techniques in the examination of electroencephalogram (EEG) data. DSP encompasses a collection of mathematical algorithms designed to employ signals, such as EEG recordings, and finds application in diverse domains, including sleep medicine, neuroscience, and biomedical engineering. Employing DSP methods for EEG data analysis enables the extraction of pertinent insights from EEG signals, the identification of event-related patterns, and the enhancement of diagnostic and therapeutic practices across various disciplines. This article provides an overview of prevalent DSP methodologies employed in EEG signal processing, encompassing filtering, power spectral analysis, wavelet analysis, independent component analysis, and artifact removal.

Digital Signal Processing Approaches in the field of Genomics: A Recent Trend

Digital signal processing (DSP) techniques have emerged as powerful tools in the field of genomics, enabling researchers to extract valuable insights from complex genetic data. This research paper presents a comprehensive analysis of the recent trends and advance- ments in applying DSP approaches to genomics. The objective is to provide an overview of the transformative role of DSP in genomic data analysis, variant calling, and interpretation. By leveraging DSP methods such as filtering, feature extraction, time-frequency analysis, and machine learning algorithms, researchers can enhance the quality of genetic signals, identify genetic variants, and gain a deeper understanding of genomic processes. The paper highlights key applications of DSP in genomics, including DNA sequence analysis, RNA expression pro- filing, epigenetics, and genome-wide association studies. Additionally, the challenges associated with applying DSP techniques in genomics, such as signal noise, data in- tegration, and computational complexity, are discussed. This research paper serves as a valuable resource for researchers, bioinformaticians, and geneticists seeking to harness the power of DSP in genomics, advancing our knowledge of genetic diseases and paving the way for personalized medicine and precision healthcare. Keywords: Digital signal processing, Genome analysis, Feature extraction, DNA sequence analysis, RNA expression profiling.

Measuring Optokinetic Reflex and Vestibulo-Ocular Reflex in Unilateral Vestibular Organ Damage Model of Zebrafish.

One-sided vestibular disorders are common in clinical practice; however, their models have not been fully established. We investigated the effect of unilateral or bilateral deficits in the vestibular organs on the vestibulo-ocular reflex (VOR) and optokinetic reflex (OKR) of zebrafish using in-house equipment. For physical dislodgement of the otoliths in the utricles of zebrafish larvae, one or both utricles were separated from the surrounding tissue using glass capillaries. The video data from VOR and OKR tests with the larvae was collected and processed using digital signal processing techniques such as fast Fourier transform and low-pass filters. The results showed that unilateral and bilateral damage to the vestibular system significantly reduced VOR and OKR. In contrast, no significant difference was observed between unilateral and bilateral damage. This study confirmed that VOR and OKR were significantly reduced in zebrafish with unilateral and bilateral vestibular damage. Follow-up studies on unilateral vestibular disorders can be conducted using this tool.

Physics-informed differentiable method for piano modeling

Numerical emulations of the piano have been a subject of study since the early days of sound synthesis. High-accuracy sound synthesis of acoustic instruments employs physical modeling techniques which aim to describe the system’s internal mechanism using mathematical formulations. Such physical approaches are system-specific and present significant challenges for tuning the system’s parameters. In addition, acoustic instruments such as the piano present nonlinear mechanisms that present significant computational challenges for solving associated partial differential equations required to generate synthetic sound. In a nonlinear context, the stability and efficiency of the numerical schemes when performing numerical simulations are not trivial, and models generally adopt simplifying assumptions and linearizations. Artificial neural networks can learn a complex system’s behaviors from data, and their application can be beneficial for modeling acoustic instruments. Artificial neural networks typically offer less flexibility regarding the variation of internal parameters for interactive applications, such as real-time sound synthesis. However, their integration with traditional signal processing frameworks can overcome this limitation. This article presents a method for piano sound synthesis informed by the physics of the instrument, combining deep learning with traditional digital signal processing techniques. The proposed model learns to synthesize the quasi-harmonic content of individual piano notes using physics-based formulas whose parameters are automatically estimated from real audio recordings. The model thus emulates the inharmonicity of the piano and the amplitude envelopes of the partials. It is capable of generalizing with good accuracy across different keys and velocities. Challenges persist in the high-frequency part of the spectrum, where the generation of partials is less accurate, especially at high-velocity values. The architecture of the proposed model permits low-latency implementation and has low computational complexity, paving the way for a novel approach to sound synthesis in interactive digital pianos that emulates specific acoustic instruments.

Digital Revolution in Telecommunications: Harnessing Advanced Electronics for Connectivity and Security

The "Digital Revolution in Telecommunications: Harnessing Advanced Electronics for Connectivity and Security" represents a critical exploration into the profound impact of advanced electronics on the contemporary telecommunications landscape. In this research paper, we navigate the intricate intersection of technology, connectivity, and security to uncover the transformative potential and challenges introduced by the digital revolution. Our investigation begins with an in-depth analysis of how advanced electronics, particularly digital signal processing and sophisticated modulation techniques, have become integral to enhancing connectivity in telecommunication networks. We explore the evolution from traditional communication systems to the era of 5G networks, emphasizing the role of digital electronics in achieving ultra-reliable low latency communications (URLLC) for diverse applications. Security takes center stage as we delve into the cryptographic underpinnings within digital electronics, focusing on encryption protocols, key management, and secure transmission methodologies. This examination aims to unveil the mechanisms safeguarding data integrity and confidentiality in the interconnected digital realm. Furthermore, we address the emergent field of quantum communication and its potential to reshape secure telecommunications. The research scrutinizes the countermeasures developed within the domain of digital electronics to mitigate risks associated with quantum advancements, ensuring the robustness of future telecommunication infrastructures. As we traverse the landscape of the digital revolution in telecommunications, this paper provides a comprehensive understanding of the synergies between advanced electronics, connectivity, and security. The insights gleaned from this research contribute to the ongoing discourse on fortifying and advancing the technological foundations of our interconnected world.

Review on Digital Signal Processing Techniques in Distributed Brillouin Fiber Sensing Systems

Review on Digital Signal Processing Techniques in Distributed Brillouin Fiber Sensing Systems

A Detailed Study on IIR-FIR Filters and Design of a Graphical User Interface for Simulation of EEG Signals

Electroencephalography (EEG) plays a pivotal role in accepting brain activity and identifying neurological disorders. The precise analysis and explanation of EEG signals require the solicitation of digital signal processing techniques, such as Infinite Impulse Response (IIR) as well as Finite IJmpulse Response (FIR) filters. This paper investigates into a complete exploration of IIR-FIR filters and their application in EEG signal processing. The first part of this research paper involves a detailed examination of IIR and FIR filter types, their mathematical foundations, pros, and cons. Various filter design methods, such as Chebyshev, Butterworth and Elliptic filters, are discussed as well as compared through literature survey. Speculative aspects, including filter design, transfer functions, and frequency responses, are presented in a clear and much accessible manner. The second phase of the study introduces the design of a Graphical User Interface (GUI) on mathematical modelling tool aimed at enabling EEG signal simulation and analysis. This GUI is designed to enable users, including researchers and clinicians, to generate synthetic EEG signals with controllable parameters, apply IIR-FIR filters in real-time, and thereby visualize the filtered signals. The interface offers user-friendly controls for customizing filter characteristics, such as filter order, cutoff frequencies, and filter type. To validate the efficiency of the designed GUI and the selected IIR-FIR filters, general simulations are conducted using EEG datasets. The results showcase the GUI's efficacy in real-time EEG signal processing, demonstrating its prospective in research, clinical diagnostics, and educational settings and many areas. In summary, this paper presents a full investigation into IIR-FIR filters, proposing insights into their theory and practical application for EEG signal processing. The development of a intelligible GUI for EEG signal simulation and study further enhances the approachability of these mathematical modelling tools to a wider audience, eventually contributing to progressions in the field of neuroscience and brain signal analysis.

Implementation of Digital Signal Processor for Pulse Doppler Radar

This paper presents the design and implementation of a digital signal processor (DSP) board, utilizing a TMS320C50 family DSP chip, for pulse-Doppler radar systems. Pulse-Doppler radar systems often encounter challenges such as strong clutter, noise, and jamming in dealing with echoes. To overcome these challenges, advanced digital signal processing techniques are employed. The main objective of this paper is to introduce a cost-effective signal processing solution that significantly enhances the performance of the radar system and brings it up to speed with modern radar technologies. The hardware described in this work can also be effectively utilized for implementing various types of signal processing algorithms. Additionally, as a secondary objective, the paper presents the digital realization of a radar detector. Traditionally, this detector was constructed using an analog Doppler filter bank. However, in this work, it is digitally implemented using N digital filters in place of the analog bank. By utilizing the designed DSP board and implementing the radar detector digitally, this paper demonstrates the potential for improved performance and efficiency in pulse-Doppler radar systems. The advancements made in this work contribute to the development of cost-effective and technologically advanced radar systems. The research results are presented for four different wind conditions, showcasing the effectiveness of the proposed approach. Furthermore, the paper suggests an algorithm that combines parametric and non-parametric techniques and provides a detailed explanation of its implementation. Finally, using the non-parametric technique, the probability of detection curves (PD) are simulated with respect to the signal-to-clutter ratio (SCR) for each wind condition, and the simulation results are depicted and compared. The achievements of this paper include the proposal of an efficient approach for clutter suppression in ground surveillance pulse Doppler radar, the comparison of different clutter suppression algorithms, and the development of an algorithm that combines parametric and non-parametric techniques. The simulation results provide valuable insights into the performance of the proposed algorithms under different wind conditions.

On the feasibility of Vis–NIR spectroscopy and machine learning for real time SARS-CoV-2 detection

The pandemic caused by Covid-19 is still present around the world. Despite advances in combating the disease, such as vaccine development, identifying infected individuals is still essential to optimize the control of human-to-human transmission of the virus. The main technique for detecting the virus is the RT-PCR method, which, despite its high relative cost, has a high accuracy in detecting the coronavirus. Given this, a method capable of performing the identification quickly, accurately, and inexpensively is necessary. Thus, this work aimed to analyze the feasibility of a new technique for identifying SARS-CoV-2 through the use of optical spectroscopy in the visible and near-infrared range (Vis–NIR) combined with machine learning algorithms. Spectral signals were obtained from nasopharyngeal swab samples previously analyzed using the RT-PCR method. The specimens were provided by the Molecular Diagnosis Laboratory of Covid-19 at Univasf. A total of 314 samples were analyzed, comprising 42 testing positive and 272 testing negative for Covid-19. Digital signal processing techniques, such as Savitzky–Golay filters and statistical methods were used to eliminate spurious elements from the original data and extract relevant features. Supervised machine learning algorithms such as SVM, Random Forest, and Naive Bayes classifiers were used to perform automatic sample identification. To evaluate the performance of the models, a 5-fold cross-validation technique was applied. With the proposed methodology, it was possible to achieve an accuracy of 75%, a sensitivity of 80%, and a specificity of 70%, in addition to an area under the ROC curve of 0.81, in the identification of nasopharyngeal swab samples from previously diagnosed individuals. From these results, it was possible to conclude that Vis–NIR spectroscopy is a promising, fast and relatively low cost technique to identify the SARS-CoV-2.

Bioinformatics procedure for investigating senolytic (anti-aging) agents: A digital signal processing technique.

Cell growth involves cell division. This stops after reaching a certain limit. Some cells become inactive and unable to undergo apoptosis (programmed cell death). These cells accumulate at sites of tissue damage or disease, thus accelerating aging. They are called senescent cells. Therapeutic interventions that can either eliminate senescent cells (senolytics) or suppress their harmful effects (senomorphics) have been developed. Senescence (aging) is caused by the inter- and intramolecular interactions between the domains of forkhead (FHD) and transactivation (TAD), as well as C-terminal region 3 (CR3) and DNA binding (DBD). On the other hand, anti-senescent/senolytic (anti-aging) activities are achieved by disrupting these interactions with CR3- and forkhead box protein O4 (FOXO4)-based peptides, such as ES2 and DRI, respectively. In this study, we use a computerized procedure based on digital signal processing to systematically analyze the inter-molecular interactions between senolytics and their targets. Informational spectrum method (ISM) is engaged. We obtained the sequences of the peptides from the interacting proteins of CR3 and FOXO4 and evaluated their ability to disrupt the inter-molecular interactions between FOXO4 and DRI and CR3 and BDB, which are responsible for senescence (aging). Our results show that the peptides have different degrees of senolytic (anti-aging) activity, depending on their affinity for CR3 and BDB, or FOXO4 and DRI. We found that enhanced senescence 2 (ES2) has a higher affinity for CR3 and BDB than FOXO4 and DRI, and that the interaction between CR3 and BDB is crucial for aging. Therefore, ES2 and other CR3-based peptides are more potent senolytics than FOXO4-based peptides. Our findings are consistent with previous studies and reveal new insights into the mechanisms of senescence and senolytics. ES2 is considered the best senolytic candidate, as it is 3-7 times more effective than DRI. We verified that ES2 has a weaker interaction with FOXO4 than CR3. However, the performance of DRI has been noted to depend on its intramolecular interactions and stability. Hence, intramolecular analyses using the digital signal processing-based technique has become very vital and will follow. CR3-based peptides are promising candidates for senolytic therapy. Senolytics are linear chains of amino acids that can target and eliminate senescent cells, which are cells that have stopped dividing and contribute to aging and age-related diseases. By using this proposed, novel computerized technique that is based on digital signal processing, senolytics can be easily analyzed and optimized for their effectiveness and safety. This provides a more rational approach to enhancing our longevity and well-being by offering interventions that can delay or reverse aging and insights that can advance the field of gerontology. This procedure also will compliment other approaches such as molecular stimulation, etc.

Ataxia severity classification using enhanced feature selection and ranking optimization through machine learning model

<span>The examination of neurological disorders and the monitoring of ataxic gait are major scientific topics that benefit from digital signal processing techniques and machine learning (ML) technologies. In this research, an ML approach is optimized with the use of Spatio-temporal data obtained from a kinect-sensor to differentiate between normal gait and ataxic. The current ML-based approaches perform very poorly because they cannot build feature-correlation among many gait characteristics. Furthermore, current ML-based techniques generate more false-positive whenever data is imbalanced in nature; especially for performing multi-label classification. This work presents a feature selection and ranking (FSR) based on extreme gradient boost (XGB) for ataxia severity classification. The FSR-XGB introduce an enhanced misclassification minimization error optimization and presents a novel feature selection and ranking to introduce feature importance using new cross-validation mechanism, both of which are aimed at solving the multi-label classification research problems. Results from experiments demonstrate that the presented FSR-XGB approach outperforms other ML-based and deep learning-based approaches.</span>

Sum Standard Deviation of Frequency – A Context Independent Machine Condition Trend Indicator

In Industry 4.0, multiple research subjects have been involved in processing enormous information generated during the manufacturing process. Meanwhile, they all pursue the same goal, that is, to maintain the manufacturing process at good operating state. Hence, many researchers are focusing their researches on continuous health monitoring using digital signal processing techniques, in particular, most researches are focused on recognition of a fault condition only. However, by the time when a fault is recognised, at least one reject has been produced, or an impact to the system's operation has occurred. In addition, many algorithms are designed specifically for certain class of applications. This paper proposes a new system health condition indicator, Sum Standard Deviation of Frequency (SSDF), which is computed from a new computational process that segments raw data streams into time segments and the segments are synchrosqueezed continuous wavelet transformed. As long as sufficient data is available, SSDF shows distinct consecutive regions for "normal", "marginal" and "abnormal" machine conditions. Actions can then be taken while the machine is in "marginal" conditions in which the manufacturing quality is still acceptable. SSDF does not link to any application context information of the raw signal data stream hence making it context independent.