Diagnostic Signals Research Articles

Increasing Electric Vehicles Reliability by Non-Invasive Diagnosis of Motor Winding Faults

The article proposes a proprietary approach to the diagnosis of induction motors allowing increasing the reliability of electric vehicles. This approach makes it possible to detect damage in the form of an inter-turn short-circuit at an early stage of its occurrence. The authors of the article describe an effective diagnostic method using the extraction of diagnostic signal features using an Enhanced Empirical Wavelet Transform and an algorithm based on the method of Ensemble Bagged Trees. The article describes in detail the methodology of the carried out research, presents the method of extracting features from the diagnostic signal and describes the conclusions resulting from the research. Phase current waveforms obtained from a real object as well as simulation results based on the field-circuit model of an induction motor were used as a diagnostic signal in the research. In order to determine the accuracy of the damage classification, simple metrics such as accuracy, sensitivity, selectivity, precision as well as complex metrics weight F1 and macro F1 were used.

Cross-Domain Transfer Learning for PCG Diagnosis Algorithm.

Cardiechema is a way to reflect cardiovascular disease where the doctor uses a stethoscope to help determine the heart condition with a sound map. In this paper, phonocardiogram (PCG) is used as a diagnostic signal, and a deep learning diagnostic framework is proposed. By improving the architecture and modules, a new transfer learning and boosting architecture is mainly employed. In addition, a segmentation method is designed to improve on the existing signal segmentation methods, such as R wave to R wave interval segmentation and fixed segmentation. For the evaluation, the final diagnostic architecture achieved a sustainable performance with a public PCG database.

МОДЕЛІ ФОРМ ПРЕДСТАВЛЕННЯ НАВЧАЮЧИХ СУКУПНОСТЕЙ ДЛЯ БАГАТОРІВНЕВИХ СИСТЕМ ДІАГНОСТУВАННЯ ВУЗЛІВ ЕЛЕКТРОТЕХНІЧНОГО ОБЛАДНАННЯ

The results of consideration of advanced mathematical models of vibration diagnostic signals are given, which take into account both the properties of diagnostic objects and the modes (speed, electric temperature, etc.) in which the object under study operates. Models of representation of training sets corresponding to certain technical states of EO units and which can work in different modes are considered. The method of representation of training sets in the form of a matrix which elements represent scattering ellipses corresponding both to certain kinds of defects of separate knots of EO, and modes of its work is offered. The structure of construction of training sets on a flat (2D) and volume (3D) matrix is ​​substantiated, the elements of which contain sets corresponding to separate units of EA, and their combination forms a separate electrotechnical unit. References 18, figures 5.

Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features

Depression is a mental disorder that continues to make life difficult or impossible for a depressed person and, if left untreated, can lead to dangerous activities such as self-harm and suicide. Nowadays, Electroencephalogram (EEG) has become an important diagnostic tool for many brain disorders. In this article, a new method for the detection of depression based on the reconstructed phase space (RPS) of EEG signals and geometrical features has been proposed. The RPS of the EEG signals of 22 normal and 22 depressed subjects are plotted in two-dimensional space and, based on their shape, 34 geometrical features are extracted. The p-values for the proposed features were significantly lower (p-value ≈ 0) indicating the capacity of the proposed geometric features for the normal and depression EEG signals classification tasks. For the purpose of reducing feature vector arrays, the performance of four optimization algorithms is checked, namely: ant colony optimization (ACO), grey wolf optimization (GWO), genetic algorithm (GA) and particle swarm optimization (PSO), in which GA with the ability of 58.8% was better than the other optimization algorithms for decreasing the feature vector arrays. Selected features are fed to the support vector machine (SVM) classifier with radial basis function (RBF) kernel and K-nearest neighbors (KNN) classifier with Euclidean and city block distances in 10-fold cross-validation (CV) strategy. The proposed framework achieved a fairly good average classification accuracy (ACC) of 99.30% and a Matthews correlation coefficient (MCC) of 0.98 using the selected features of the PSO algorithm and the SVM classifier. We found that the RPS of normal EEG signals has a more irregular, complex and unpredictable shape than the RPS of depression EEG signals which has more regular (simple) with less variation and more predictable shape; therefore, we can say that RPS of EEG signals can be used as a biomarker for psychiatrists which are simpler than the EEG signals in visual depression diagnostics. We also found that EEG signals from the right hemisphere are significant for depression detection than the left hemisphere. The proposed framework may be used in clinics and hospitals to detect depression disorder quickly and precisely.

Mass-scale emotionality reveals human behaviour and marketplace success.

Online reviews promise to provide people with immediate access to the wisdom of the crowds. Yet, half of all reviews on Amazon and Yelp provide the most positive rating possible, despite human behaviour being substantially more varied in nature. We term the challenge of discerning success within this sea of positive ratings the 'positivity problem'. Positivity, however, is only one facet of individuals' opinions. We propose that one solution to the positivity problem lies with the emotionality of people's opinions. Using computational linguistics, we predict the box office revenue of nearly 2,400 movies, sales of 1.6 million books, new brand followers across two years of Super Bowl commercials, and real-world reservations at over 1,000 restaurants. Whereas star ratings are an unreliable predictor of success, emotionality from the very same reviews offers a consistent diagnostic signal. More emotional language was associated with more subsequent success.

Time-Frequency Domain Reflectometry for Live HTS Cable System via Inductive Couplers and Neural Network

High-temperature superconducting (HTS) cable has become one of the most promising technology along with an adaptation of a unique superconducting conductor that allows long-distance transmission of the large volume of electronic power with less power loss. Diagnostic and monitoring technique for the reliable and safe operation of HTS cable system has also become a crucial factor to commercialize the HTS cable system in the regarding field. HTS cable system which needs a cooling process for re-activating superconductors is more affected by the loss of cost and time. This paper is proposing the time-frequency domain reflectometry (TFDR) based diagnostic technique for a live HTS cable system. By adapting inductive couplers, the live HTS cable system and the diagnostic signal would communicate without direct contact. Signal attenuation and distortion during the signal induction are compensated by LSTM-based neural network. The proposed method is applied to an AC 154 kV 600 MVA HTS cable system in and is further verified through Advanced Design System (ADS) software.

Mass spectrometry glycophenotype characterization of ALG2-CDG in Argentinean patients with a new genetic variant in homozygosis.

Human ALG2 encodes an α 1,3mannosyltransferase that catalyzes the first steps in the synthesis of N-glycans in the endoplasmic reticulum. Variants in ALG2cause a congenital disorder of glycosylation (CDG) known as ALG2-CDG. Up to date, nine ALG2-CDG patients have been reported worldwide. ALG2-CDG is a rare autosomal recessive inherited disorder characterized by neurological involvement, convulsive syndrome of unknown origin, axial hypotonia, and mental and motor regression. In this study, we used MALDI-TOF MS to define both total serum protein and transferrin (Tf) N-glycan phenotypes in three ALG2-CDG patients carrying a c.752G > T, p.Arg251Leu ALG2 missense variant in homozygous state, as determined by exome sequencing. Comparing it to control samples, we have observed Tf under-occupancy of glycosylation site(s) typical of a defective N-glycan assembly and the occurrence of oligomannose and hybrid type N-glycans. Moreover, we have observed a slight oligomannose accumulation in total serum glyco-profiles. The increased heterogeneity of serum N-glycome in the studied patients suggests a marginal disarrangement of the glycan processing in ALG2-CDG. Previous studies reported on slightly increased concentrations of abnormal serum N-glycans in CDG-I due to defects in the mannosylation steps of dolichol-linked oligosaccharide biosynthesis. This preliminary work aims at considering serum N-glycan accumulation of high mannosylated glycoforms, such as oligomannose and hybrid type N-glycans, as potential diagnostic signals for ALG2-CDG patients.

Detection of inferior myocardial infarction based on morphological characteristics

Early accurate detection of inferior myocardial infarction is an important way to reduce the mortality from inferior myocardial infarction. Regrading the existing problems in the detection of inferior myocardial infarction, complex model structures and redundant features, this paper proposed a novel inferior myocardial infarction detection algorithm. Firstly, based on the clinic pathological information, the peak and area features of QRS and ST-T wavebands as well as the slope feature of ST waveband were extracted from electrocardiogram (ECG) signals leads Ⅱ, Ⅲ and aVF. In addition, according to individual features and the dispersion between them, we applied genetic algorithm to make judgement and then input the feature with larger degree into support vector machine (SVM) to realize the accurate detection of inferior myocardial infarction. The proposed method in this paper was verified by Physikalisch-Technische Bundesanstalt (PTB) diagnostic electrocardio signal database and the accuracy rate was up to 98.33%. Conforming to the clinical diagnosis and the characteristics of specific changes in inferior myocardial infarction ECG signal, the proposed method can effectively make precise detection of inferior myocardial infarction by morphological features, and therefore is suitable to be applied in portable devices development for clinical promotion.

Diffusion and Relaxation Edited Proton NMR Spectroscopy of Plasma Reveals a High-Fidelity Supramolecular Biomarker Signature of SARS-CoV-2 Infection.

We have applied nuclear magnetic resonance spectroscopy based plasma phenotyping to reveal diagnostic molecular signatures of SARS-CoV-2 infection via combined diffusional and relaxation editing (DIRE). We compared plasma from healthy age-matched controls (n = 26) with SARS-CoV-2 negative non-hospitalized respiratory patients and hospitalized respiratory patients (n = 23 and 11 respectively) with SARS-CoV-2 rRT-PCR positive respiratory patients (n = 17, with longitudinal sampling time-points). DIRE data were modelled using principal component analysis and orthogonal projections to latent structures discriminant analysis (O-PLS-DA), with statistical cross-validation indices indicating excellent model generalization for the classification of SARS-CoV-2 positivity for all comparator groups (area under the receiver operator characteristic curve = 1). DIRE spectra show biomarker signal combinations conferred by differential concentrations of metabolites with selected molecular mobility properties. These comprise the following: (a) composite N-acetyl signals from α-1-acid glycoprotein and other glycoproteins (designated GlycA and GlycB) that were elevated in SARS-CoV-2 positive patients [p = 2.52 × 10–10 (GlycA) and 1.25 × 10–9 (GlycB) vs controls], (b) two diagnostic supramolecular phospholipid composite signals that were identified (SPC-A and SPC-B) from the –+N–(CH3)3 choline headgroups of lysophosphatidylcholines carried on plasma glycoproteins and from phospholipids in high-density lipoprotein subfractions (SPC-A) together with a phospholipid component of low-density lipoprotein (SPC–B). The integrals of the summed SPC signals (SPCtotal) were reduced in SARS-CoV-2 positive patients relative to both controls (p = 1.40 × 10–7) and SARS-CoV-2 negative patients (p = 4.52 × 10–8) but were not significantly different between controls and SARS-CoV-2 negative patients. The identity of the SPC signal components was determined using one and two dimensional diffusional, relaxation, and statistical spectroscopic experiments. The SPCtotal/GlycA ratios were also significantly different for control versus SARS-CoV-2 positive patients (p = 1.23 × 10–10) and for SARS-CoV-2 negatives versus positives (p = 1.60 × 10–9). Thus, plasma SPCtotal and SPCtotal/GlycA are proposed as sensitive molecular markers for SARS-CoV-2 positivity that could effectively augment current COVID-19 diagnostics and may have value in functional assessment of the disease recovery process in patients with long-term symptoms.

The intelligent system for monitoring and technical diagnostics of complex technological equipment in the agro-industrial complex

The article considers the intelligent system of control and technical diagnostics of complex technological equipment and flexible production systems developed by the authors. The provision of automatic selection of important parameters from the entire set of input and output parameters is due to the additional training of the neural network in the process and perhaps by increasing-decreasing the number of active neurons that do not lead to deterioration in the quality of technical diagnostics, but also due to the choice of excess neurons and their activation by retraining or failure of the neurons of the network. Diagnostic systems may include sensors, a computer system, and diagnostic signal display devices. The computational part of the system can contain modules implemented with the possibility of intelligent analysis and containing a dynamic model that is implemented on a trained neural network, and a module implemented with the possibility of additional training of the neural network and the selection of active and redundant neurons.

Residual Learning Diagnosis Detection: An Advanced Residual Learning Diagnosis Detection System for COVID-19 in Industrial Internet of Things.

Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention. Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is expensive and time-consuming. To build an efficient and valid alternative of NAT, this article investigates the feasibility of employing computed tomography images of lungs as the diagnostic signals. Unlike normal lungs, parts of the lungs infected with the COVID-19 developed lesions, ground-glass opacity, and bronchiectasis became apparent. Through a public dataset, in this article, we propose an advanced residual learning diagnosis detection (RLDD) scheme for the COVID-19 technique, which is designed to distinguish positive COVID-19 cases from heterogeneous lung images. Besides the advantage of high diagnosis effectiveness, the designed residual-based COVID-19 detection network can efficiently extract the lung features through small COVID-19 samples, which removes the pretraining requirement on other medical datasets. In the test set, we achieve an accuracy of 91.33%, a precision of 91.30%, and a recall of 90%. For the batch of 150 samples, the assessment time is only 4.7s. Therefore, RLDD can be integrated into the application programming interface and embedded into the medical instrument to improve the detection efficiency of COVID-19.

Spider diagram and Analytical GREEnness metric approach for assessing the greenness of quantitative 1H-NMR determination of lamotrigine: Taguchi method based optimization

An optimized eco-friendly quantitative proton nuclear magnetic resonance (1H NMR) method was developed for determination of lamotrigine. Taguchi method was utilized as a chemometric approach for the first time to optimize different NMR experimental parameters. The optimum acquisition conditions were found to be 128 number of scans, relaxation delay 1 s and pulse angle 90° using L16 (45) orthogonal array. The diagnostic doublet signal at 7.7 in the 1H NMR spectra was selected as quantitative peak for determination of lamotrigine. NMR spectra were obtained in dimethyl sulfoxide-d6 using phloroglucinol as internal standard. The content of lamotrigine was measured within the range of 5–50 μg/mL. The limits of detection (LOD) and limits of quantitation (LOQ) were 0.62 and 1.87 μg/mL respectively. The proposed method was successfully applied for determination of lamotrigine in tablet dosage form without interference from the commonly encountered excipients. The greenness of the developed method was assessed using Greenness Index with spider diagram, National Environmental Methods Index (NEMI), Analytical Eco-Scale and Analytical GREEnness metric approach (AGREE), it was found to be excellent green analytical method. By comparing with other reported methods, this study was the greenest one according to NEMI labeling and Analytical Eco-Scale score and the second one according to AGREE which ensure that quantitative NMR is a recent green analytical technique.

Low-Cost Diagnosis of Rotor Asymmetries of Induction Machines at Very Low Slip With the Goertzel Algorithm Applied to the Rectified Current

Induction machines are essential components of many industrial installations and, therefore, their faults must be detected early. Fault detection using current spectrum analysis is attracting an increasing interest as a condition-based monitoring technique. However, its use to detect rotor asymmetries in high-power induction machines, which operate at very low slip, is particularly challenging, due to the closeness of the characteristic fault harmonics to the fundamental component, separated only a few mHz. Their reliable detection in harsh industrial environments requires a very high spectral resolution, that is, long acquisition times and a huge number of current samples, what hinders its implementation on embedded, online devices with limited computing resources. To address this problem, this paper presents a novel combination of diagnostic techniques, the use of the rectified current as diagnostic signal, and the Goertzel algorithm as signal processing tool. This unique combination allows for an optimized implementation ot the Goertzel algorithm, which provides a high spectral resolution in the full load range of the machine, with a low computational cost and a negligible memory footprint. This proposal is validated experimentally with the fault diagnosis of a high-power medium-voltage industrial motor.

Shaft hoist rope wear condition as a diagnostic signal

In the paper there were discussed factors which determine wear process intensity of shaft hoist rope. The ropes as the element or the elements set are appearing in numerous, diverse machines and mechanisms, fulfilling the most different functions in them. Mining shaft hoists are a very representative example of the object being used for a vertical transport, of both the staff of the mine and winning. Results of experimental investigations of rope were presented. Findings obtained different methods were compared.

Математична модель коробки перемикання передач типу ЯМЗ-239 як об’єкта діагностування

Features of the operation of road transport provide for frequent stops and further acceleration, braking, gear changes, and the like. This leads to an increase in the dynamic load of vehicle units, and, as a consequence, to an intensification of the processes of wear of the friction surfaces of clutch discs, gear and cardan gears, and the like. Defects of the components of the gear box also arise due to improper use. Timely and high-quality diagnostics of the car’s gear box will increase the uptime of the car and improve its technical and economic indicators. A significant increase in the number of vehicles, an increase in brands and models require the development of modern diagnostic tools and methods. The value of the general principles of operation of gear boxes allows you to localize the area of malfunctions, minimize the process of trial and error. Now, in most cases, malfunctions of the YaMZ-239 type gear box are detected by external signs. It is possible to improve the quality of the process of diagnosing gear boxes, among other things, by creating new mathematical models of their functioning. That the existing methods and means of diagnosing gear boxes of motor vehicles do not fully allow determining their current technical condition, which requires the development of mathematical models of their units and parts as an object of diagnostics it was found when analyzing the literary sources. The YMZ-239 type gear box, which is part of the power unit of trucks, was selected as the object of diagnostics. An overview of the designs of the model range of gear boxes of the YaMZ-239 type, as objects of diagnostics are presents in the article. Replacing real technical devices with their idealized models makes it possible to widely apply various mathematical methods. In this case, the gear box, as an object of diagnostics, is presented in the form of a "black box", the input and output parameters of which have a finite set of values. To represent this diagnostic object in the form of a "black box", the following are set: the number of all incoming actions Y; the number of all output signs of malfunction S; the number of all faults of the diagnostic object X; operator A, which converts the quantities X and Y into the number S. In general, the mathematical model is a system of functional dependencies between each diagnostic signal and structural parameters. For the YaMZ-239 gear box, a diagnosis matrix has been compiled, which includes a list of faults and symptoms of faults. That the process of making a diagnosis based on the model of the object being diagnosed is possible if the reverse transformation of the number of signs of malfunctions into the number of structural parameters (malfunctions) of the object was unambiguous it has been determined. The proposed mathematical model for diagnosing a gear box of the YaMZ-239 type will make it possible to identify malfunctions of its assemblies and parts, depending on their signs, which will significantly increase the uptime of both the gear box itself and the entire vehicle.

РАЗРАБОТКА ПРЕДЛОЖЕНИЙ ПО ВЫБОРУ АНСАМБЛЯ ШИРОКОПОЛОСНЫХ СИГНАЛОВ ДЛЯ СИСТЕМ СПУТНИКОВОЙ СВЯЗИ С МНОГОСТАНЦИОННЫМ ДОСТУПОМ И КОДОВЫМ РАЗДЕЛЕНИЕМ КАНАЛОВ

When designing satellite communication lines and calculating service areas, any unfavorable combinations of signal propagation conditions are taken into account, which, during operation of the repeater, leads to a significant underestimation of the calculated signal level in comparison with the actually provided one. Under these conditions, control of the compliance of the declared characteristics with the real level of the power flux density created by the satellite repeater at the earth’s surface in order to ensure electromagnetic compatibility is the most priority task. When conducting radio monitoring of satellite communication lines using active diagnostic methods, it is necessary to rationally determine the parameters of diagnostic signals so that they meet the conflicting requirements for their confident reception after being relayed by a satellite without disrupting (reducing the capacity) of the functioning of satellite communication lines. Mathematical modeling of the retransmission path of diagnostic signals in the multi-station repeater mode, taking into account the relative spatial location of the satellite, the service area and the radio monitoring station, has been carried out. The model takes into account most of the known factors and features of the propagation of radio signals in satellite communication lines in relation to various statistical phenomena in the atmosphere (refraction, signal depolarization), as well as the instability of the position of the satellite in orbit and the associated variability of the boundaries of the coverage area. As a result of the simulation, it was concluded that under conditions of a priori uncertainty about the polarization parameters used on board the monitored satellite of the antennas, there is a fundamental possibility of active diagnostics of the repeater shafts without weakening the diagnostic signal due to polarization mismatch.

Application of fractal theory in diagnostics of composite materials

The article presents the results of studies to identify the features of diagnostic acoustic signals when defects are detected in composite materials using the methods of the theory of fractals. The use of the analysis of the spectral power density (PSD) and wavelet spectra of these signals did not lead to the detection of significant quantitative differences in the acoustic signals during the process of sample diagnostics (panel 4 mm thick). Acoustic signals from the standpoint of analyzing the characteristics of multifractal spectra were analyzed. It is shown that the defect state of the sample corresponds to the monofractal characteristic of the multifractal spectrum.

γ-Glutamyl transpeptidase-activatable near-infrared nanoassembly for tumor fluorescence imaging-guided photothermal therapy.

Rationale: Precise treatment of tumors is attracting increasing attention. Molecular probes simultaneously demonstrating the diagnostic signal and pharmacological effect in response to tumor microenvironment are highly desired. γ-glutamyl transpeptidase (GGT) is a biomarker with significantly up-regulated expression in the tumor area. We developed a GGT responsive near-infrared (NIR) nanoassembly for tumor-specific fluorescence imaging-guided photothermal therapy.Methods: The GGT responsive NIR probe was constructed by conjugating GGT-specific substrate γ-glutamic acid (γ-Glu) with cyanine fluorophore (NRh-NH2) via amide reaction. The resulting NRh-G spontaneously assembled into nanoparticles (NRh-G-NPs) around 50 nm. The NPs were characterized and the properties evaluated in the presence or absence of GGT. Subsequently, we studied fluorescence imaging and photothermal therapy of NRh-G-NPs in vitro and in vivo.Results: NRh-G-NPs, upon specific reaction with GGT, turned into NRh-NH2-NPs, showing a ~180-fold fluorescence enhancement and excellent photothermal effect recovery. NRh-G-NPs could selectively light up U87MG tumor cells while their fluorescence was weak in L02 human normal liver cells. The NPs also showed excellent tumor cell ablation upon laser irradiation. After intravenous injection into tumor-bearing mice, NRh-G-NPs could arrive in the tumor area and specifically light up the tumor. Following laser irradiation, the tumor could be completely erased with no tumor reoccurrence for up to 40 days.Conclusions: NRh-G-NPs were specifically responsive to GGT overexpressed in U87MG tumor cells and selectively lit up the tumor for imaging-guided therapy. Besides, the recovery of photothermal property in the tumor area could improve cancer therapy precision and decreased side effects in normal tissues.

Recent Advances in Photosensitizers as Multifunctional Theranostic Agents for Imaging-Guided Photodynamic Therapy of Cancer.

In recent years tremendous effort has been invested in the field of cancer diagnosis and treatment with an overall goal of improving cancer management, therapeutic outcome, patient survival, and quality of life. Photodynamic Therapy (PDT), which works on the principle of light-induced activation of photosensitizers (PS) leading to Reactive Oxygen Species (ROS) mediated cancer cell killing has received increased attention as a promising alternative to overcome several limitations of conventional cancer therapies. Compared to conventional therapies, PDT offers the advantages of selectivity, minimal invasiveness, localized treatment, and spatio-temporal control which minimizes the overall therapeutic side effects and can be repeated as needed without interfering with other treatments and inducing treatment resistance. Overall PDT efficacy requires proper planning of various parameters like localization and concentration of PS at the tumor site, light dose, oxygen concentration and heterogeneity of the tumor microenvironment, which can be achieved with advanced imaging techniques. Consequently, there has been tremendous interest in the rationale design of PS formulations to exploit their theranostic potential to unleash the imperative contribution of medical imaging in the context of successful PDT outcomes. Further, recent advances in PS formulations as activatable phototheranostic agents have shown promising potential for finely controlled imaging-guided PDT due to their propensity to specifically turning on diagnostic signals simultaneously with photodynamic effects in response to the tumor-specific stimuli. In this review, we have summarized the recent progress in the development of PS-based multifunctional theranostic agents for biomedical applications in multimodal imaging combined with PDT. We also present the role of different imaging modalities; magnetic resonance, optical, nuclear, acoustic, and photoacoustic in improving the pre-and post-PDT effects. We anticipate that the information presented in this review will encourage future development and design of PSs for improved image-guided PDT for cancer treatment.

Biodegradable FeWOx nanoparticles for CT/MR imaging-guided synergistic photothermal, photodynamic, and chemodynamic therapy.

The development of a simple and effective single constituent multifunctional nanotheranostic platform producing a multimodality diagnostic signal and curing effect is still a challenge. Herein, we synthesized simple and biodegradable FeWOx ternary oxide nanoparticles and modified their surface with RGD-PEG-NH2 (FeWOx-PEG-RGD NPs), whereby resulting NPs possessed a (T2/T1) switchable MRI/CT dual-modal imaging ability and synergistic photothermal therapy (PTT)/photodynamic therapy (PDT)/chemodynamic therapy (CDT) capacity. We showed that FeWOx-PEG-RGD NPs enabled tumor accumulation under a magnetic field drive and RGD-mediated tumor penetration and implemented PTT/PDT treatment under 980 nm laser irradiation. In an acidic tumor microenvironment (TME) with a high hydrogen peroxide (H2O2) expression, NPs degraded to release Fe3+ and Fe2+, triggering a Fenton reaction to generate ˙OH for CDT. The released Fe2+ led to T2/T1 signal conversion for tracing cancer therapy, while the high X-ray attenuation coefficient of W also made it a good CT contrast agent for guided therapy. Thus, the structurally simple FeWOx-PEG-RGD was capable of mediating (T2/T1-weighted) MR/CT two modal imaging-guided PTT/PDT/CDT synergic therapy with a high accuracy and superb anticancer efficiency. The simple, degradable, rapid-clearance, and multifunctional FeWOx-PEG-RGD NPs provide a novel, promising, and versatile nanotheranostic platform.