Online feature learning for condition monitoring of rotating machinery

Martin-del-Campo, Sergio; Sandin, Fredrik

doi:10.1016/j.engappai.2017.06.012

Cited by 44 publications

(22 citation statements)

References 32 publications

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“…Thus, low-power solutions enabling learning and recognition of complex patterns with less energy has many potential applications, for example in embedded intelligence and deep edge sensor systems. In particular, ultra-low power solutions operating at the order of milliwatts is a key enabler for advanced wireless sensor systems, for example for machine monitoring (Martin del Campo and Sandin, 2017;Martin del Campo et al, 2013) where the system needs to operate autonomously with limited resources over the expected lifetime of the monitored machine (Häggström, 2018;. Searching for effective SNN architectures for pattern recognition that are suitable for implementation in ultra-low power dynamic neuromorphic processors like the DYNAP-SE, we adapted and investigated the aforementioned cricket auditory feature detection circuit.…”

Section: Discussionmentioning

confidence: 99%

Synaptic Delays for Insect-Inspired Temporal Feature Detection in Dynamic Neuromorphic Processors

Sandin

Nilsson

2020

Front. Neurosci.

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Spiking neural networks implemented in dynamic neuromorphic processors are well suited for spatiotemporal feature detection and learning, for example in ultra low-power embedded intelligence and deep edge applications. Such pattern recognition networks naturally involve a combination of dynamic delay mechanisms and coincidence detection. Inspired by an auditory feature detection circuit in crickets, featuring a delayed excitation by postinhibitory rebound, we investigate disynaptic delay elements formed by inhibitory-excitatory pairs of dynamic synapses. We configure such disynaptic delay elements in the DYNAP-SE neuromorphic processor and characterize the distribution of delayed excitations resulting from device mismatch. Furthermore, we present a network that mimics the auditory feature detection circuit of crickets and demonstrate how varying synapse weights, input noise and processor temperature affects the circuit. Interestingly, we find that the disynaptic delay elements can be configured such that the timing and magnitude of the delayed postsynaptic excitation depend mainly on the efficacy of the inhibitory and excitatory synapses, respectively. Delay elements of this kind can be implemented in other reconfigurable dynamic neuromorphic processors and opens up for synapse level temporal feature tuning with large fan-in and flexible delays of order 10-100 ms.

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Section: Discussionmentioning

confidence: 99%

Synaptic Delays for Insect-Inspired Temporal Feature Detection in Dynamic Neuromorphic Processors

Sandin

Nilsson

2020

Front. Neurosci.

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“…This section describes other AI-based early fault diagnosis methods. Martin-del-Campo et al [171] used dictionary learning to automatically derive signal features for characterizing different operational conditions and faults of rolling bearings. Almeida et al [172] used time-domain features as input of generic multi-layer perceptron for bearing fault identification.…”

Section: Other Methodsmentioning

confidence: 99%

“…Martin-del-Campo et al [171] Dictionary learning Almeida et al [172] Time-domain features + generic multi-layer perceptron Li et al [173] Wavelet transformation + ant colony optimization Brkovic et al [174] Wavelet transformation + quadratic classifier Li et al [175] Fuzzy lattice neurocomputing Cruz-Vega et al [176] Discrete wavelet + binary classification tree Martínez-Rego et al [177] Time domain features + one-class classifier…”

Section: Authors Methodologiesmentioning

confidence: 99%

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

174

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Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

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“…A discussion on the effects of varying the learning step size is found in. 48 Furthermore, an evaluation of additional sparse coding algorithms, in addition to matching pursuit, using the same data as in this evaluation is available in. 30 Time-domain features.…”

Section: %mentioning

confidence: 99%

Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning

Martin-del-Campo

Sandin

Schnabel³

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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We analyze vibration signals from wind turbines with dictionary learning and investigate the relation between dictionary distances and faults occurring in a wind turbine output shaft rolling element bearing and gearbox under different data and compute constraints. Dictionary learning is an unsupervised machine learning method for signal processing, which permits learning a set of signal-specific features that have been used to monitor the condition of rotating machines, including wind turbines. Dictionary distance is one such feature, and its effectiveness depends on an adequate selection of the dictionary learning hyperparameters and the data availability, which typically is constrained in condition monitoring systems for remotely located wind farms. Here we evaluate the characteristics of the dictionary distance feature under healthy and faulty conditions of the wind turbines using different options for the selection of the pretrained dictionary, the sparsity of the signal model which determines the compute requirements, and the interval between data samples. Furthermore, we compare the dictionary distance feature to the typical time-domain features used in condition monitoring. We find that the dictionary distance based feature of a faulty wind turbine deviates by a factor of two or more from the population distribution several weeks before the gearbox bearing fault was reported, using a data sampling interval as long as 24 h and a model sparsity as low as 2.5%.

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Online feature learning for condition monitoring of rotating machinery

Cited by 44 publications

References 32 publications

Synaptic Delays for Insect-Inspired Temporal Feature Detection in Dynamic Neuromorphic Processors

Synaptic Delays for Insect-Inspired Temporal Feature Detection in Dynamic Neuromorphic Processors

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning

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