A Review: Condition Based Techniques and Predictive Maintenance for Motor

Manjare, Aniket A.; Patil, Babasaheb G.

doi:10.1109/icais50930.2021.9395903

Cited by 13 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This reference life corresponds to the maximum radial load acting on the bearings used in the experiment. (6) The transformations used are linear, i.e., the time-to-fail at high stress is multiplied by a constant (the acceleration factor) to obtain the equivalent time-to-fail at use stress [17][18]. (7) The bearing failure (potential) probability is 0% below the alert limit of vibration, and it is 100% at and above the alarm limit.…”

Section: Failure Analysis Discussion and Resultsmentioning

confidence: 99%

“…Electrical motors are integral parts of the majority of the machines installed in an industrial plant [3,6]. The failure of any one of these motors degrades the machine performance, which, in turn, influences the overall plant availability.…”

Section: Introductionmentioning

confidence: 99%

“…The failure of any one of these motors degrades the machine performance, which, in turn, influences the overall plant availability. Manjare et al [6] reviewed machine learning (ML)-based fault detection techniques and predictive maintenance (PdM)/CBM strategies for electrical motors used in industrial plants. Furthermore, the vibration data acquired from accelerometer sensors are extensively used for data analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Maintenance Initiation Prediction Incorporating Vibrations and System Availability

Gopikuttan

Veettil²,

Govindan³

2022

Adv. technol. innov.

View full text Add to dashboard Cite

As per ISO-10816, electric motors up to 15 kW are classified as Class I machines, and the major reason for their failure is that the vibrations in them are above the alert limit. This study presents a new model for predicting the condition-based maintenance (CBM) initiation points through vibration measurement in a system of Class I machines. The proposed model follows the accelerated life testing (ALT) procedure. ALT includes the formation of an artificial wear environment in bearings to analyze the resultant system vibrations on system availability. The artificial wear environment created is close to the real industrial situation. The results show that the prediction of the CBM initiation points is based on the established relation between the system availability and vibrations. Furthermore, a relation between the available time for maintenance initiation and different vibration velocities is demonstrated.

show abstract

Section: Failure Analysis Discussion and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Maintenance Initiation Prediction Incorporating Vibrations and System Availability

Gopikuttan

Veettil²,

Govindan³

2022

Adv. technol. innov.

View full text Add to dashboard Cite

show abstract

“…Traditional maintenance practices based on fixed schedules or usage-based approaches may lead to either unnecessary maintenance actions or unexpected failures. Condition-based maintenance (CBM) has emerged as a promising strategy for improving the efficiency, reliability, and cost-effectiveness of maintenance practices across various industries [3]. By leveraging advanced sensing, data analytics, and predictive modeling techniques, CBM can be incorporated into an integrated health and usage monitoring system (HUMS) to enable the monitoring of critical parameters and the timely detection of defects, allowing for targeted maintenance interventions and optimizing the lifecycle management of SRMs [4].…”

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring of Solid Rocket Motors: From Destructive Testing to Perspectives of Photonic-Based Sensing

Korompili,

Mußbach,

Riziotis

2024

Instruments

View full text Add to dashboard Cite

In the realm of space exploration, solid rocket motors (SRMs) play a pivotal role due to their reliability and high thrust-to-weight ratio. Serving as boosters in space launch vehicles and employed in military systems, and other critical & emerging applications, SRMs’ structural integrity monitoring, is of paramount importance. Traditional maintenance approaches often prove inefficient, leading to either unnecessary interventions or unexpected failures. Condition-based maintenance (CBM) emerges as a transformative strategy, incorporating advanced sensing technologies and predictive analytics. By continuously monitoring crucial parameters such as temperature, pressure, and strain, CBM enables real-time analysis, ensuring timely intervention upon detecting anomalies, thereby optimizing SRM lifecycle management. This paper critically evaluates conventional SRM health diagnosis methods and explores emerging sensing technologies. Photonic sensors and fiber-optic sensors, in particular, demonstrate exceptional promise. Their enhanced sensitivity and broad measurement range allow precise monitoring of temperature, strain, pressure, and vibration, capturing subtle changes indicative of degradation or potential failures. These sensors enable comprehensive, non-intrusive monitoring of multiple SRM locations simultaneously. Integrated with data analytics, these sensors empower predictive analysis, facilitating SRM behavior prediction and optimal maintenance planning. Ultimately, CBM, bolstered by advanced photonic sensors, promises enhanced operational availability, reduced costs, improved safety, and efficient resource allocation in SRM applications.

show abstract

“…Consequentially, the analysis of maintenance procedures will focus on these distinctive electric loads. The main principles of this action are indicated in [1,2], while in [3][4][5], its limits and performances are revealed.…”

Section: Introductionmentioning

confidence: 99%

Smart Predictive Maintenance Device for Critical In-Service Motors

2022

View full text Add to dashboard Cite

The paper proposed an innovative predictive maintenance system, designated to monitor and diagnose critical electrical equipment (generally large power electric motors) within industrial electrical installations. A smart and minimally invasive system is designed and developed. Its scope is to evaluate continuously the essential operating parameters (electrical, thermal, and mechanical) of the investigated equipment. It manages to report the deviations of inspected machine operating parameters values from the rated ones. The system also suggests the potential cause of these abnormal variations along with possible means (if the defect is identified in a database, constantly updated with each appearance of a malfunction). The developed maintenance device generates an operating report of the analyzed equipment, in which the values of power quality and energy indicators are computed and interpreted. Additionally, real-time remote transmission of analyzed data is facilitated, making them accessible from any location. The proposed maintenance system is a low-cost device that is easy to install and use in comparison with similar existing devices and equipment. The designed maintenance system was tested on dedicated to low-voltage equipment up to 100 kW.

show abstract

A Review: Condition Based Techniques and Predictive Maintenance for Motor

Cited by 13 publications

References 18 publications

Maintenance Initiation Prediction Incorporating Vibrations and System Availability

Maintenance Initiation Prediction Incorporating Vibrations and System Availability

Structural Health Monitoring of Solid Rocket Motors: From Destructive Testing to Perspectives of Photonic-Based Sensing

Smart Predictive Maintenance Device for Critical In-Service Motors

Contact Info

Product

Resources

About