ExaMon-X: A Predictive Maintenance Framework for Automatic Monitoring in Industrial IoT Systems

Borghesi, Andrea; Burrello, Alessio; Bartolini, Andrea

doi:10.1109/jiot.2021.3125885

Cited by 12 publications

(3 citation statements)

References 50 publications

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“…It relies on Eclipse Mosquitto as a message broker, which implements the Message Queuing Telemetry Transport (MQTT) network-based messaging protocol. Collected data are fed to Examon [40], an open-source 6 framework that enables performance ad energy monitoring of HPC systems. Examon relies on Grafana as interactive data-visualization platform with live-updated dashboards.…”

Section: Plant Simulationmentioning

confidence: 99%

ControlPULP: A RISC-V On-Chip Parallel Power Controller for Many-Core HPC Processors with FPGA-Based Hardware-In-The-Loop Power and Thermal Emulation

Ottaviano

Balas

Bambini

et al. 2023

Preprint

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High-Performance Computing (HPC) processors are nowadays integrated Cyber-Physical Systems demanding complex and high-bandwidth closed-loop power and thermal control strategies. To efficiently satisfy real-time multi-input multi-output (MIMO) optimal power requirements, high-end processors integrate an on-die power controller system (PCS). While traditional PCSs are based on a simple microcontroller (MCU)-class core, more scalable and flexible PCS architectures are required to support advanced MIMO control algorithms for managing the ever-increasing number of cores, power states, and process, voltage, and temperature variability. This paper presents ControlPULP, an open-source, HW/SW RISC-V parallel PCS platform consisting of a single-core MCU with fast interrupt handling coupled with a scalable multi-core programmable cluster accelerator and a specialized DMA engine for the parallel acceleration of real-time power management policies. ControlPULP relies on FreeRTOS to schedule a reactive power control firmware (PCF) application layer. We demonstrate ControlPULP in a power management use-case targeting a next-generation 72-core HPC processor.We first show that the multi-core cluster accelerates the PCF, achieving 4.9x speedup compared to single-core execution, enabling more advanced power management algorithms within the control hyper-period at a shallow area overhead, about 0.1\% the area of a modern HPC CPU die. We then assess the PCS and PCF by designing an FPGA-based, closed-loop emulation framework that leverages the heterogeneous SoCs paradigm, achieving DVFS tracking with a mean deviation within 3\% the plant's thermal design power (TDP) against a software-equivalent model-in-the-loop approach. Finally, we show that the proposed PCF compares favorably with an industry-grade control algorithm under computational-intensive workloads.

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Section: Plant Simulationmentioning

confidence: 99%

ControlPULP: A RISC-V On-Chip Parallel Power Controller for Many-Core HPC Processors with FPGA-Based Hardware-In-The-Loop Power and Thermal Emulation

Ottaviano

Balas

Bambini

et al. 2023

Preprint

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“…PULP Controller is a low-level power controller, works transparently to the application, user, and system software, currently targeting EPI processors. As for the monitor, DCDB [25] and EXAMON [9] will be used, both of which support in-band/out-of-band monitoring properties as well as the functionalities to analyze/model the monitored data. Some of the tools have been already integrated, and other integration is under construction.…”

Section: Our Ongoing Efforts On Software Tool Integrationmentioning

confidence: 99%

On the Convergence of Malleability and the HPC PowerStack: Exploiting Dynamism in Over-Provisioned and Power-Constrained HPC Systems

Arima

Comprés

Schulz

2022

Lecture Notes in Computer Science

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Recent High-Performance Computing (HPC) systems are suffering from severe problems, such as massive power consumption, while at the same time significantly under-utilized system resources. Given the power consumption trends, future systems will be deployed in an over-provisioned manner where more resources are installed than they can afford to power simultaneously. In such a scenario, maximizing resource utilization and energy efficiency, while keeping a given power constraint, is pivotal. Driven by this observation, in this position paper we first highlight the recent trends of resource management techniques, with a particular focus on malleability support (i.e., dynamically scaling resource allocations/requirements for a job), co-scheduling (i.e., colocating multiple jobs within a node), and power management. Second, we consider putting them together, assess their relationships/synergies, and discuss the functionality requirements in each software component for future over-provisioned and power-constrained HPC systems. Third, we briefly introduce our ongoing efforts on the integration of software tools, which will ultimately lead to the convergence of malleability and power management, as it is designed in the HPC PowerStack initiative.

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“…Under the fixed-schedule maintenance mode, if the equipment suddenly fails, it often causes a great influence such as the wasting of manpower and the increase in costs. In another way, predictive maintenance (PdM) extracts the crucial features from the physical parameters to produce a health indicator through a modelling and validation process [8][9][10][11]. In general, the typical parameters of manufacturing automation equipment include the vibration, sound noise, temperature, voltage and current, etc.…”

Section: Introductionmentioning

confidence: 99%

A Robot-Operation-System-Based Smart Machine Box and Its Application on Predictive Maintenance

Chang,

Chai,

et al. 2023

Sensors

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Predictive maintenance is a proactive approach to maintenance in which equipment and machinery are monitored and analyzed to predict when maintenance is needed. Instead of relying on fixed schedules or reacting to breakdowns, predictive maintenance uses data and analytics to determine the appropriate time to perform maintenance activities. In industrial applications, machine boxes can be used to collect and transmit the feature information of manufacturing machines. The collected data are essential to identify the status of working machines. This paper investigates the design and implementation of a machine box based on the ROS framework. Several types of communication interfaces are included that can be adopted to different sensor modules for data sensing. The collected data are used for the application on predictive maintenance. The key concepts of predictive maintenance include data collection, a feature analysis, and predictive models. A correlation analysis is crucial in a feature analysis, where the dominant features can be determined. In this work, linear regression, a neural network, and a decision tree are adopted for model learning. Experimental results illustrate the feasibility of the proposed smart machine box. Also, the remaining useful life can be effectively predicted according to the trained models.

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ExaMon-X: A Predictive Maintenance Framework for Automatic Monitoring in Industrial IoT Systems

Cited by 12 publications

References 50 publications

ControlPULP: A RISC-V On-Chip Parallel Power Controller for Many-Core HPC Processors with FPGA-Based Hardware-In-The-Loop Power and Thermal Emulation

ControlPULP: A RISC-V On-Chip Parallel Power Controller for Many-Core HPC Processors with FPGA-Based Hardware-In-The-Loop Power and Thermal Emulation

On the Convergence of Malleability and the HPC PowerStack: Exploiting Dynamism in Over-Provisioned and Power-Constrained HPC Systems

A Robot-Operation-System-Based Smart Machine Box and Its Application on Predictive Maintenance

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