Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques

In this paper, a novel noncontact and nonintrusive framework experimental method is used for the monitoring and the diagnosis of a three phase’s induction motor faults based on an infrared thermography technique (IRT). The basic structure of this work begins with this applying IRT to obtain a thermograph of the considered machine. Then, bag-of-visual-word (BoVW) is used to extract the fault features with Speeded-Up Robust Features (SURF) detector and descriptor from the IRT images. Finally, various faults patterns in the induction motor are automatically identified using an ensemble learning called Extremely Randomized Tree (ERT). The proposed method effectiveness is evaluated based on the experimental IRT images, and the diagnosis results show its capacity and that it can be considered as a powerful diagnostic tool with a high classification accuracy and stability compared to other previously used methods.

Punctual mechanical oscillation in modulation of muscular tonus in children with spasticity

Spasticity is a motor condition present in 75 to 88% of children with Cerebral Palsy (CP). One form of treatment is called punctual mechanical oscillation (PO). The current study aimed to study different protocols for the application of PO and the magnitude of their effects. In total, 7children with medical diagnosis of CP and ICD (International Classification of Diseases) were included. The first intervention protocol (Int1) consisted of the application of PO to the spastic muscle tendon and the second intervention protocol (Int2) to the muscle belly ofthe spastic antagonist muscle. For evaluation, the Modified Ashworth Scale (MAS) was used, while simultaneously capturing the mechanomyography (MMG) signals. Data were collected pre-intervention and 1 (Post1), 15 (Post15), 30 (Post30), 45 (Post45), and60 (Post60) minutes after the interventions. The MAS values (median ± interquartile range) post intervention were statistically lower when compared to the pre values in the 2 protocols studied; in Int1between Pre (2 ± 0) andPost15 (0 ± 1.75), Post30 (0 ± 1), Post45 (1 ± 1),and Post60 (1 ± 1), and in Int2only between Pre (2 ± 1) and Post1 (0 ± 1).The values found in the MMG in both its temporal and spectral domains did not follow a pattern (p>0.05). The comparison between the protocols did not demonstrate statistical differences in any characteristics (MAS, MMGMF, and MMGRMS). However, PO was shown to be a therapeutic resource that modulated spasticity for up to 60 minutes after its application, and PO could contribute as a tool to aid the treatment of spasticity.

Optimization of valve plate applied in secondary unit and noise reduction

Secondary unit, in term of application, is the combination of pump and motor, the valve plate specifically designed for pump or motor may not be suitable for a secondary unit. This paper mainly discusses design of valve plate of secondary unit applied in mobile crane, several critical points needed to be noticed during design have been discussed, furthermore, by adopting the proposed method to optimize a valve plate, which originates from closed-loop pump and now is used in opened-loop system, noise reduction was realized. Firstly, 1D simulation models, including pump condition and motor condition, were established in AMESim to obtain cylinder pressure, flow ripple and other critical parameters; secondly, by using Pumplinx, 3D numerical simulation was conducted to evaluate the cavitation risk; finally, a test bed was set up to validate the simulation result. Simulation result agreed well with the tested one. Both of them verified practicability of the proposed method. This research may provide a guidance for engineers and scholars who are interested in pump and motor.

Sensorless Speed Estimation for the Diagnosis of Induction Motors via MCSA. Review and Commercial Devices Analysis

Sensorless speed estimation has been extensively studied for its use in control schemes. Nevertheless, it is also a key step when applying Motor Current Signature Analysis to induction motor diagnosis: accurate speed estimation is vital to locate fault harmonics, and prevent false positives and false negatives, as shown at the beginning of the paper through a real industrial case. Unfortunately, existing sensorless speed estimation techniques either do not provide enough precision for this purpose or have limited applicability. Currently, this is preventing Industry 4.0 from having a precise and automatic system to monitor the motor condition. Despite its importance, there is no research published reviewing this topic. To fill this gap, this paper investigates, from both theoretical background and an industrial application perspective, the reasons behind these problems. Therefore, the families of sensorless speed estimation techniques, mainly conceived for sensorless control, are here reviewed and thoroughly analyzed from the perspective of their use for diagnosis. Moreover, the algorithms implemented in the two leading commercial diagnostic devices are analyzed using real examples from a database of industrial measurements belonging to 79 induction motors. The analysis and discussion through the paper are synthesized to summarize the lacks and weaknesses of the industry application of these methods, which helps to highlight the open problems, challenges and research prospects, showing the direction in which research efforts have to be made to solve this important problem.

Artificial neural network–based fault diagnosis for induction motors under similar, interpolated and extrapolated operating conditions

The diagnosis of mechanical and electrical faults of induction motors (IMs) has been performed using artificial neural networks (ANN) for similar, interpolated and extrapolated operating speeds. The current and vibration signals of faulty and healthy IMs measured from a Machinery Fault Simulator are used in this work. In total, ten different IM fault conditions have been considered: four mechanical faults (bearing fault, unbalanced rotor, misaligned rotor, and bowed rotor), five electrical faults (broken rotor bar, phase unbalanced fault with two severity levels, and stator winding fault with two severity levels), and one healthy motor condition. An ANN model is developed in which raw time domain data of faulty IMs are used and the fault diagnosis is then performed for the motor’s various operating conditions. Initially, diagnosis is performed to predict and classify the motor faults, for the same operating conditions for which we trained ANN. The diagnosis is then extended for interpolated and extrapolated speeds in order to accomplish the diagnosis when data are not available at all the required operating speeds. From the results, it is found that the present ANN-based diagnosis is effective in the same speed case for various operating conditions (seven speeds as well as three loads). In addition, the diagnosis is found to be satisfactory for all interpolated and extrapolated speed cases. It is also observed that the present IM fault diagnosis is better in the interpolation speed cases than the extrapolation speed cases.

MRCP as a Biomarker of Motor Action with Varying Degree of Central and Peripheral Contribution as Defined by Ultrasound Imaging

Introduction: Motor imagination is an alternative rehabilitation strategy for people who cannot execute real movements. However it is still a matter of debate to which degree it involves activation of deeper muscle structures, which cannot be detected by surface electromyography (SEMG). Methods: Eighteen able bodied participants performed cue based isometric ankle plantar flexion (active movement) followed by active relaxation under four conditions: executed movements with two levels of muscle contraction (fully executed and attempted movements, EM and AM) and motor imagination with and without detectable muscle twitches (IT and I). Most prominent peaks and distinctive phases of Movement Related Cortical Potential (MRCP) were compared between conditions. Ultrasound imagining (USI) and SEMG were used to detect movements. Results: IT showed spatially distinctive significant difference compared to both I and AM during active movement preparation and re-afferentation phase; further wide spread differences were found between IT and AM during active movement execution and posteriorly during preparation for active relaxation. EM and AM showed largest difference frontally during active movement planning and posteriorly during executing of active relaxation. Movement preparation positivity P1 showed significant difference in amplitude between IT and AM but not between IT and I. Conclusion: USI can detect subliminal movements (twitches) better than SEMG. MRCP is a biomarker sensitive to different levels of muscle contraction and relaxation. IT is a motor condition distinguishable from both I and AM. Significance: EEG biomarkers of movements could be used to identify pathological conditions, that manifest themselves during either active contraction or active relaxation.

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.

A New Tool to Study Parkinsonism in the Context of Aging: MPTP Intoxication in a Natural Model of Multimorbidity

The diurnal rodent Octodon degus (O. degus) is considered an attractive natural model for Alzheimer’s disease and other human age-related features. However, it has not been explored so far if the O. degus could be used as a model to study Parkinson’s disease. To test this idea, 10 adult male O. degus were divided into control group and MPTP-intoxicated animals. Motor condition and cognition were examined. Dopaminergic degeneration was studied in the ventral mesencephalon and in the striatum. Neuroinflammation was also evaluated in the ventral mesencephalon, in the striatum and in the dorsal hippocampus. MPTP animals showed significant alterations in motor activity and in visuospatial memory. Postmortem analysis revealed a significant decrease in the number of dopaminergic neurons in the ventral mesencephalon of MPTP animals, although no differences were found in their striatal terminals. We observed a significant increase in neuroinflammatory responses in the mesencephalon, in the striatum and in the hippocampus of MPTP-intoxicated animals. Additionally, changes in the subcellular expression of the calcium-binding protein S100β were found in the astrocytes in the nigrostriatal pathway. These findings prove for the first time that O. degus are sensitive to MPTP intoxication and, therefore, is a suitable model for experimental Parkinsonism in the context of aging.

A synthetic kinematic index of trunk displacement conveying the overall motor condition in Parkinson’s disease

Parkinson's disease (PD) is characterized by motor impairment, affecting quality of life and increasing fall risk, due to ineffective postural control. To this day, the diagnosis remains based on clinical approach. Similarly, motor evaluation is based on heterogeneous, operator-dependent observational criteria. A synthetic, replicable index to quantify motor impairment is still lacking. Hence, we have designed a new measure of postural stability which assesses the trunk displacement in relation to the center of mass, that we named trunk displacement index (TDI). Twenty-three PD patients and twenty-three healthy controls underwent motor examination through a stereophotogrammetric system. A correlation analysis was performed to assess the relationship of TDI with gait parameters and clinical motor scale (UPDRS-III). The TDI sensitivity was estimated, comparing pre- and post- L-DOPA subclinical dose intake. The TDI showed significant correlations with many gait parameters and with the UPDRS-III. Furthermore, the TDI resulted capable in discriminating between off and on state in PD, whereas gait parameters failed two show any difference between those two conditions. Our results suggest that the TDI may be considered a highly sensitive biomechanical index, reflecting the overall motor condition in PD, and provided of clinical relevance due to the correlation with the clinical evaluation.