Identification of Inability States of Rotating Machinery Subsystems Using Industrial IoT and Convolutional Neural Network – Initial Research

Rotating parts can be found in almost all operational equipment in the industry and are of great importance for proper operation. However, reliability theory explains that every industrial system can change its state when failure happens. Predictive maintenance as one of the latest maintenance strategy emerged from the Maintenance 4.0 concept. Nowadays, this concept can include Industrial Internet of Things (IIoT) devices to connect industrial assets thus enable data collection and analysis that can help make better decisions about maintenance activity. Robust data acquisition system is a prerequisite for any modern predictive maintenance task as it provides necessary data for further analysis and health assessment of the industry asset. Fault diagnosis is an important task in the maintenance of industrial rotating subsystems, considering that early state change diagnosis and fault identification can prevent system failure. Vibration analysis in theory and practice is considered a correct technique for early detection of state changes and failure diagnostics of rotating subsystems. The identified technical state should be considered in a context of the ability and different inability states. Therefore, early different inability states identification is the next step in the rotary machinery diagnostics procedure. Most of the existing techniques for fault diagnosis of rotating subsystems that use vibrations involve the step of extracting features from the raw signal. Considering that the features that describe the behavior of the rotary subsystem can differ significantly depending on the type of equipment, such an approach usually requires an expert in the field of signal processing and rotary subsystems who can define the necessary features. Recently, the emergence of machine deep learning and its application in maintenance promises to provide highly efficient fault diagnostics while simultaneously reducing the need for expert knowledge and human labour. This paper presents authors aim to use self-developed IIoT system built as an IIoT accelerometer as the edge device, web API and database with convolutional neural network as deep learning-based data-driven fault diagnosis to detect and identify different inability states of rotating subsystems. Large dataset for two different rotational speed is collected using IIOT system and multiple convolutional neural network models are trained and tested to examine possibility of using IIOT for inability state prediction

Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

Cyber-physical production systems (CPPS) integrate physical and computational resources due to increasingly available sensors and processing power. This enables the usage of data, to create additional benefit, such as condition monitoring or optimization. These capabilities can lead to cognition, such that the system is able to adapt independently to changing circumstances by learning from additional sensors information. Developing a reference architecture for the design of CPPS and standardization of machines and software interfaces is crucial to enable compatibility of data usage between different machine models and vendors. This paper analysis existing reference architecture regarding their cognitive abilities, based on requirements that are derived from three different use cases. The results from the evaluation of the reference architectures, which include two instances that stem from the field of cognitive science, reveal a gap in the applicability of the architectures regarding the generalizability and the level of abstraction. While reference architectures from the field of automation are suitable to address use case specific requirements, and do not address the general requirements, especially w.r.t. adaptability, the examples from the field of cognitive science are well usable to reach a high level of adaption and cognition. It is desirable to merge advantages of both classes of architectures to address challenges in the field of CPPS in Industrie 4.0

A high level e-maintenance architecture to support on-site teams

Emergent architectures and paradigms targeting reconfigurable manufacturing systems increasingly rely on intelligent modules to maximize the robustness and responsiveness of modern installations. Although intelligent behaviour significantly minimizes the occurrence of faults and breakdowns it does not exclude them nor can prevent equipment’s normal wear. Adequate maintenance is fundamental to extend equipments’ life cycle. It is of major importance the ability of each intelligent device to take an active role in maintenance support. Further this paradigm shift towards “embedded intelligence”, supported by cross platform technologies, induces relevant organizational and functional changes on local maintenance teams. On the one hand, the possibility of outsourcing maintenance activities, with the warranty of a timely response, through the use of pervasive networking technologies and, on the other hand, the optimization of local maintenance staff are some examples of how IT is changing the scenario in maintenance. The concept of e-maintenance is, in this context, emerging as a new discipline with defined socio-economic challenges. This paper proposes a high level maintenance architecture supporting maintenance teams’ management and offering contextualized operational support. All the functionalities hosted by the architecture are offered to the remaining system as network services. Any intelligent module, implementing the services’ interface, can report diagnostic, prognostic and maintenance recommendations that enable the core of the platform to decide on the best course of action.manufacturing systems, platform technologies, maintenance

Practical applications of multi-agent systems in electric power systems

The transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur

Coal mine low power laser methane detection and alarm instrument

At present, the portable carrier catalytic methane detection and alarm instrument for coal mine generally has many problems, such as high power consumption, short standby time, low detection accuracy, few parameters and single function, which can not meet the rapid development needs of mine safety. In this paper, a low power portable laser methane detection and alarm instrument based on tunable laser absorption spectroscopy (TDLAS) is designed. The instrument can detect methane concentration, ambient temperature and ambient pressure at the same time. It has the functions of sound and light alarm, historical data storage and query, and integrates Wi-Fi to realize data wireless transmission. The instrument can work continuously for 36 hours, and the response time is less than 15 seconds. It has the function of self-diagnosis. The overall performance of the instrument has been greatly improved compared with the traditional mine methane portable instrument. A mobile methane alarm Internet of things(IOT) system for coal mine based on portable instrument has been developed, which realizes real-time upload of data and cloud analysis, makes the traditional mine gas monitoring and control system powerfully supplemented, greatly improves the detection level of coal mine gas, and has broad application prospects

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security