IIoT Machine Health Monitoring Models for Education and Training

IoT, IIoT and Industry 4.0 technologies are leading the way for digital transformation in manufacturing, healthcare, transportation, energy, retail, cities, supply chain, agriculture, buildings, and other sectors. Machine health monitoring and predictive maintenance of rotating machines is an innovative IIoT use case in the manufacturing and energy sectors. This chapter covers how machine health monitoring can be implemented using advanced sensor technology as a basis for predictive maintenance in rotating devices. It also covers how sensor data can be collected from the devices at the edge, preprocessed in a microcontroller/edge node, and sent to the cloud or local server for advanced data intelligence. In addition, this chapter describes the design and operation of three innovative models for education and training supporting the accelerated adoption of these technologies in industry sectors.

Using Learned Health Indicators and Deep Sequence Models to Predict Industrial Machine Health

In this paper, we describe a machine learning approach for predicting machine health indicators with a large time horizon into the future. The approach uses state-of-the-art neural network architectures for sequence modelling and can incorporate numerical-sensor and categorical data using entity embeddings. Moreover, we describe an unsupervised labelling approach where classes are generated using continuous sensor values in the training data and a clustering algorithm. To validate our approach, we performed an ablation study to verify the effectiveness of each of our model’s components. In this context, we show that entity embeddings can be used to generate effective features from categorical inputs, that state-of-the-art models, while originally developed for a different set of problems, can nonetheless be transferred to perform industrial asset health classification and provide a performance boost over simpler networks that have been traditionally used, such as relatively shallow recurrent or convolutional networks. Taken together, we present a machine health monitoring system that can accurately generate asset health predictions. This system can incorporate both numerical and categorical information, the current state-of-the-art for sequence modelling, and generate labels in an unsupervised fashion when explicit labels are unavailable.

A Review on Anomaly Detection in Time Series

Time series is a very common class of data sets. Among others, it is very simple to obtain time series data from a variety of various science and finance applications and an anomaly detection technique for time series is becoming a very prominent research topic nowadays. Anomaly identification covers intrusion detection, detection of theft, mistake detection, machine health monitoring, network sensor event detection or habitat disturbance. It is also used for removing suspicious data from the data set before production. This review aims to provide a detailed and organized overview of the Anomaly detection investigation. In this article we will first define what an anomaly in time series is, and then describe quickly some of the methods suggested in the past two or three years for detection of anomaly in time series

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

As an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

A proposed data acquisition system and algorithm for signal processing of moving-coil geophone’s output

The study of different types of vibrational and seismic movements is important for exploration in strata monitoring, machine health monitoring, earthquake detection, etc. In order to study these vibrational movements, it needs to be acquired first for analysis. Data acquisition using the seismic sensors is a challenging task. This paper presents a data acquisition system developed to acquire seismic signals from a moving-coil geophone. The paper also discusses a signal interpretation algorithm that is devised to perform automatic detection of a seismic event occurrence by separating through the waveform and non-waveform components in the sensor’s output using Gaussian naive Bayes classifier and Kernel density estimation technique. The proposed method is effective in the identification of a useful signal and identification of its nature of origin. Accuracy of the algorithm was 99% for the waveform classification. Sensitivity of the data acquisition system for the seismic sensors was 1.589 µm s–1. Further, the developed data acquisition system and the algorithm can be used in mines for seismological studies aimed at separating the vibration signal generated due to explosion and the one caused due to Earth’s tectonic and seismic activities.