Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals

Soualhi, Moncef; Nguyen, Khanh T. P.; Soualhi, Abdenour; Medjaher, Kamal; Hemsas, Kamel Eddine

doi:10.1016/j.measurement.2019.03.065

Cited by 78 publications

(23 citation statements)

References 37 publications

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“…Improving availability has established itself as one of the major challenges for industrial companies of our time (Elasha, Shanbr, Li, and Mba, 2019). The anticipation of failures (preventive maintenance), now at the heart of the maintenance activity allows a real improvement in the availability and reliability of systems (Sammouri, 2014;Soualhi, Nguyen, Soualhi, Medjaher, and Hemsas, 2019). Therefore, it is necessary for the industrialists to predict the ideal moment to intervene.…”

Section: Introductionmentioning

confidence: 99%

Integration of future maintenance actions in the prediction parameters of the ATLAS COPCO ZR 200 compressor

Tjahè

Fotso²,

Medard³

et al. 2021

IJPHM

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The prediction of several failure modes of an industrial equipment requires the development of prediction systems with several interdependent parameters. The integration of future maintenance actions with this type of prediction system is a major asset for maintenance decision making. This is even more relevant in the event that after having predicted the future occurrence of several failure modes, the maintenance department does not have the necessary resources to correct all the predicted failure modes at once. In this case it becomes necessary to know how much longer the equipment will work if future partial maintenance actions that do not correct all failure modes are implemented. It is to contribute to the resolution of this problem that we propose an architecture integrating the future maintenance actions to the prediction of several interdependent parameters. This architecture is based on the association of Proportional Integral Derivative regulators to Neuro-Fuzzy systems taking into account the four previous instants to predict the next instant. An application is made with accuracies of the order of 70% for the prediction of the phenomena of fouling of the coolers and of the order of 90% for the prediction of the phenomena of clogging of the filters of the ATLAS COPCO compressor, this with Central Processing Unit values not exceeding one minute.

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Section: Introductionmentioning

confidence: 99%

Integration of future maintenance actions in the prediction parameters of the ATLAS COPCO ZR 200 compressor

Tjahè

Fotso²,

Medard³

et al. 2021

IJPHM

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show abstract

“…Analyzing the fault data sensed by the wearable leads to inaccurate decision making. As the health data must be more reliable, the adaptive neuro-fuzzy inference system (ANFIS) is presented to diagnose health data [9]. A distributed similarity test has been introduced to detect the sensor data fault [10].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection

Ijaz

Wang

et al. 2020

Electronics

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Wearable technology plays a key role in smart healthcare applications. Detection and analysis of the physiological data from wearable devices is an essential process in smart healthcare. Physiological data analysis is performed in fog computing to abridge the excess latency introduced by cloud computing. However, the latency for the emergency health status and overloading in fog environment becomes key challenges for smart healthcare. This paper resolves these problems by presenting a novel tri-fog health architecture for physiological parameter detection. The overall system is built upon three layers as wearable layer, intelligent fog layer, and cloud layer. In the first layer, data from the wearable of patients are subjected to fault detection at personal data assistant (PDA). To eliminate fault data, we present the rapid kernel principal component analysis (RK-PCA) algorithm. Then, the faultless data is validated, whether it is duplicate or not, by the data on-looker node in the second layer. To remove data redundancy, we propose a new fuzzy assisted objective optimization by ratio analysis (FaMOORA) algorithm. To timely predict the user’s health status, we enable the two-level health hidden Markov model (2L-2HMM) that finds the user’s health status from temporal variations in data collected from wearable devices. Finally, the user’s health status is detected in the fog layer with the assist of a hybrid machine learning algorithm, namely SpikQ-Net, based on the three major categories of attributes such as behavioral, biomedical, and environment. Upon the user’s health status, the immediate action is taken by both cloud and fog layers. To ensure lower response time and timely service, we also present an optimal health off procedure with the aid of the multi-objective spotted hyena optimization (MoSHO) algorithm. The health off method allows offloading between overloaded and underloaded fog nodes. The proposed tri-fog health model is validated by a thorough simulation performed in the iFogSim tool. It shows better achievements in latency (reduced up to 3 ms), execution time (reduced up to 1.7 ms), detection accuracy (improved up to 97%), and system stability (improved up to 96%).

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“…Reference [24] is based on the current signal of the feed motor of the CNC milling machine to monitor the tool groove break during end milling. Reference [25] based on three-phase current analysis, extracted characteristic indicators for gear fault detection and diagnosis. Reference [26] built a planetary gear fault feature extraction model based on deep neural networks, and verified the effectiveness of planetary gear fault identification.…”

Section: Introductionmentioning

confidence: 99%

“…Mainly for the identification and evaluation of spurring gears backlash failure in meshing teeth. For designing and building an experimental bench, collecting the current signals of the servo drive motor, extracting the time and frequency domain features reflecting the changes in the current signal [25], and establishing the mapping relationship between the backlash and the current signal. In the steady speed phase, multiple features are extracted from different backlash current signals, and the sensitivity is evaluated based on the FDA [32,33] method to find the most sensitive state quantity for backlash.…”

Section: Introductionmentioning

confidence: 99%

Gear Backlash Detection and Evaluation Based on Current Characteristic Extraction and Selection

Yang¹,

Liu²,

Deng³

2020

IEEE Access

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Motor Current Signal Analysis (MCSA) is widely used in the monitoring of electric motors, but few researchers applied it in the transmission system because of the fault information loss and aliasing of current signals. The purpose of the research is to analyze the influence of the gear backlash under different size effect on the current signal from the permanent magnet synchronous motor (PMSM). And it supplies a convenient route to detect the gear backlash more economy. In most cases, the current of PMSM is usually changed with the operating states, which may relate with rotor speed, load and gear backlash. So, it is important to extract the sensitive features under different states. Here, the stable speed and variable speed states are considered at first. At the steady stage, multiple features are extracted from current signals under variable backlash. And fisher discriminant analysis (FDA) is introduced to evaluate and select the most sensitivity features for backlash are selected. Then, features at variable speed states are extracted. Because the transient change of the current frequency and phase adjustable, it is very difficult to obtain the steady state response characteristic. In this paper, the inverse of the time difference between the positive peaks of the signal at this phase is utilized as the characteristic index describing phase. Furthermore, the polynomial principle is combined to enhance the characteristics of the extracted features. Therefore, the mapping relationship between the backlash and the current signal of the servo motor is established under different speed stages. The results show that by monitoring the motor current, it is feasible and effective to distinguish the different backlash of the meshing gear. INDEX TERMS MCSA, gear backlash, servo motor, current signal, feature extraction.

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Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals

Abstract: OATAO is an open access repository that collects the work of some Toulouse researchers and makes it freely available over the web where possible.

Cited by 78 publications

References 37 publications

Integration of future maintenance actions in the prediction parameters of the ATLAS COPCO ZR 200 compressor

Integration of future maintenance actions in the prediction parameters of the ATLAS COPCO ZR 200 compressor

Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection

Gear Backlash Detection and Evaluation Based on Current Characteristic Extraction and Selection

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