A low-cost predictive maintenance approach for E/E/PE dependable system

2015 ◽  
pp. 897-905
Author(s):  
Antonio da Silva ◽  
Paulo Cugnasca
Keyword(s):  
Low Cost ◽  
Predictive Maintenance

Low cost predictive maintenance

1993 ◽  
Vol 26 (6) ◽  
pp. 329
Author(s):  
Schenck Limited
Keyword(s):  
Low Cost ◽  
Predictive Maintenance

scholarly journals Relevance of Drift Components and Unit-to-Unit Variability in the Predictive Maintenance of Low-Cost Electrochemical Sensor Systems in Air Quality Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3298
Author(s):  
Georgi Tancev
Keyword(s):  
Air Quality ◽  
Anomaly Detection ◽  
Low Cost ◽  
Predictive Maintenance ◽  
Quality Monitoring ◽  
Calibration Data ◽  
Sensor Systems ◽  
Air Quality Monitoring ◽  
Measurement Results ◽  
Unsupervised Anomaly Detection

As key components of low-cost sensor systems in air quality monitoring, electrochemical gas sensors have recently received a lot of interest but suffer from unit-to-unit variability and different drift components such as aging and concept drift, depending on the calibration approach. Magnitudes of drift can vary across sensors of the same type, and uniform recalibration intervals might lead to insufficient performance for some sensors. This publication evaluates the opportunity to perform predictive maintenance solely by the use of calibration data, thereby detecting the optimal moment for recalibration and improving recalibration intervals and measurement results. Specifically, the idea is to define confidence regions around the calibration data and to monitor the relative position of incoming sensor signals during operation. The emphasis lies on four algorithms from unsupervised anomaly detection—namely, robust covariance, local outlier factor, one-class support vector machine, and isolation forest. Moreover, the behavior of unit-to-unit variability and various drift components on the performance of the algorithms is discussed by analyzing published field experiments and by performing Monte Carlo simulations based on sensing and aging models. Although unsupervised anomaly detection on calibration data can disclose the reliability of measurement results, simulation results suggest that this does not translate to every sensor system due to unfavorable arrangements of baseline drifts paired with sensitivity drift.

scholarly journals Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1109
Author(s):  
Christian Gianoglio ◽  
Edoardo Ragusa ◽  
Andrea Bruzzone ◽  
Paolo Gastaldo ◽  
Rodolfo Zunino ◽  
...  
Keyword(s):  
High Voltage ◽  
Low Cost ◽  
Economic Loss ◽  
Predictive Maintenance ◽  
Electrical Machine ◽  
Embedded Devices ◽  
Maintenance Program ◽  
Monitoring Process ◽  
Insulation Status

The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenance program. Partial discharges (PDs) phenomena affect the insulation system of an electrical machine and—in the long term—can lead to a breakdown, with a consequent, significant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is specifically designed to run on low-cost embedded devices.

scholarly journals The “DOLPHINS” Project: A Low-Cost Real-Time Multivariate Process Control From Large Sensor Arrays Providing Sparse Binary Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Eugenio Alladio ◽  
Marcello Baricco ◽  
Vincenzo Leogrande ◽  
Renato Pagliari ◽  
Fabio Pozzi ◽  
...  
Keyword(s):  
Real Time ◽  
Binary Data ◽  
Low Cost ◽  
Sensor Arrays ◽  
Predictive Maintenance ◽  
Machine Learning Techniques ◽  
Shop Floor ◽  
Time Range ◽  
Maintenance Technicians ◽  
Optimal Approach

The “DOLPHINS” project started in 2018 under a collaboration between three partners: CNH Industrial Iveco (CHNi), RADA (an informatics company), and the Chemistry Department of the University of Turin. The project’s main aim was to establish a predictive maintenance method in real-time at a pilot plant (CNHi Iveco, Brescia, Italy). This project currently allows maintenance technicians to intervene on machinery preventively, avoiding breakdowns or stops in the production process. For this purpose, several predictive maintenance models were tested starting from databases on programmable logic controllers (PLCs) already available, thus taking advantage of Machine Learning techniques without investing additional resources in purchasing or installing new sensors. The instrumentation and PLCs related to the truck sides’ paneling phase were considered at the beginning of the project. The instrumentation under evaluation was equipped with sensors already connected to PLCs (only on/off switches, i.e., neither analog sensors nor continuous measurements are available, and the data are in sparse binary format) so that the data provided by PLCs were acquired in a binary way before being processed by multivariate data analysis (MDA) models. Several MDA approaches were tested (e.g., PCA, PLS-DA, SVM, XGBoost, and SIMCA) and validated in the plant (in terms of repeated double cross-validation strategies). The optimal approach currently used involves combining PCA and SIMCA models, whose performances are continuously monitored, and the various models are updated and tested weekly. Tuning the time range predictions enabled the shop floor and the maintenance operators to achieve sensitivity and specificity values higher than 90%, but the performance results are constantly improved since new data are collected daily. Furthermore, the information on where to carry out intervention is provided to the maintenance technicians between 30 min and 3 h before the breakdown.

scholarly journals An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3781 ◽  
Author(s):  
Michael Short ◽  
John Twiddle
Keyword(s):  
Real Time ◽  
Condition Monitoring ◽  
Large Scale ◽  
Sliding Mode ◽  
Low Cost ◽  
Field Testing ◽  
Predictive Maintenance ◽  
Water Industry ◽  
Time Condition ◽  
Pumping Equipment

This paper is concerned with the implementation and field-testing of an edge device for real-time condition monitoring and fault detection for large-scale rotating equipment in the UK water industry. The edge device implements a local digital twin, processing information from low-cost transducers mounted on the equipment in real-time. Condition monitoring is achieved with sliding-mode observers employed as soft sensors to estimate critical internal pump parameters to help detect equipment weasr before damage occurs. The paper describes the implementation of the edge system on a prototype microcontroller-based embedded platform, which supports the Modbus protocol; IP/GSM communication gateways provide remote connectivity to the network core, allowing further detailed analytics for predictive maintenance to take place. The paper first describes validation testing of the edge device using Hardware-In-The-Loop techniques, followed by trials on large-scale pumping equipment in the field. The paper concludes that the proposed system potentially delivers a flexible and low-cost industrial digitalization platform for condition monitoring and predictive maintenance applications in the water industry.

scholarly journals Low-Cost Online Contact Resistance Measurement of Power Connectors to Ease Predictive Maintenance

2019 ◽  
Vol 68 (12) ◽  
pp. 4825-4833 ◽  
Author(s):  
Akash Kadechkar ◽  
Manuel Moreno-Eguilaz ◽  
Jordi-Roger Riba ◽  
Francesca Capelli
Keyword(s):  
Contact Resistance ◽  
Low Cost ◽  
Predictive Maintenance ◽  
Resistance Measurement

Nuclear Operating Experience Review and User Needs Assessment for a Risk-informed Predictive Maintenance Human System Interface

2021 ◽  
Vol 65 (1) ◽  
pp. 1600-1604
Author(s):  
Thomas A. Ulrich ◽  
Torrey Mortenson
Keyword(s):  
Data Analytics ◽  
Power Plants ◽  
Low Cost ◽  
Operating Experience ◽  
Combined Cycle ◽  
Nuclear Industry ◽  
Predictive Maintenance ◽  
Diagnostic Process ◽  
Human System ◽  
Vibration Data

The U.S. commercial nuclear industry is facing increasing economic pressure due to low-cost alternatives such as natural gas combined-cycle combustion power plants and renewable energy technologies such as wind and solar. More efficient and intelligent operations and maintenance strategies can yield substantial economic gains through a proposed risk-informed predictive maintenance strategy. The strategy leverages plant process, maintenance, and vibration data with advanced data analytics to monitor and predict condition degradations in support of preventative maintenance. This paper documents the operating experience review of the existing monitoring and diagnostic process. A semi-structured series of interviews assessed current practices, tools, shortcomings, and anlaysts’ needs to support the development of a human system interface (HSI) for the advanced data analytics approach. The results of the interviews are reported as feature requirements for the proposed HSI system which is actively under development as part of this ongoing project.

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