Wireless Condition Monitoring System for Rotating Machinery Powered by a Hybrid Vibration Based Energy Harvester / Mohd Sofwan Mohd Resali and Hanim Salleh

This paper presents the development of wireless condition monitoring system for rotating machinery powered by a hybrid vibration based energy harvester. The self-powered condition monitoring system consists of three parts. The first part of the system is the energy harvester, the second part is the power management and the third part is the android based user interface. The system used a hybrid energy harvester (piezoelectric and electromagnetic) to harvest energy from the vibrating machine at a resonance frequency of 50±2 Hz and 0.25g ms-2 of acceleration. The maximum output power from the hybrid harvester was 3.00 mW at 200 kΩ of load resistor. The power management circuit efficiency was 85% with output power of 2.55 mW. An accelerometer sensor and a temperature sensor were connected to the power management unit to sense the vibration and temperature level of the machine. Data from the sensors were transmitted through the wireless Bluetooth dongle to the android phone for end user monitoring. An android application was developed to receive the acceleration and temperature condition monitoring. At maximum power, initial charging duration of the supercapacitor was 130 seconds, and duration for recharging to 8.2V was 15 seconds. Therefore, the self-powered system managed to transmit data to the android application 15 second’s intervals

Design of a Wireless Sensor Node for Vibration Monitoring of Industrial Machinery

Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis

Remote machine condition monitoring based on power supply measurements

The most widely used rotating machines in the industry are three phase alternative current (AC) induction machines. With the advances in variable speed drive (VSD) technology, they have become even more reliable than their direct current (DC) counterpart. However, inevitably these motors soon begin to fail with time due to mechanical, electrical or thermal stress hence the need for condition monitoring (CM). Condition monitoring systems help keep machines running productively by detecting potential equipment failures before it actually fails. Many condition monitoring methods exist on the market including vibration monitoring; acoustic emission monitoring, thermal monitoring, chemical monitoring, current monitoring but most of these methods require additional sensors and expensive data acquisition system on top of a specialise software tool. This all increases the cost of ownership and maintenance. For more efficient monitoring of induction motor drive systems, this research investigates an innovative remote monitoring system using existing data available in AC drives based on AC motor operating process. This research uses standard automation components already present in most automated control systems. A remote data communication platform is developed, allowing access to the control data remotely over a wireless network and internet using PLC and SCADA system. Remote machine condition monitoring is not a new idea but its application to machine monitoring based on power supply parameters indirectly measured by an inverter is new. To evaluate the basic performance of the platform, the monitoring of shaft misalignment, a typical fault in mechanical system is investigated using an in-house gearbox test rig. It has resulted in a model based detection method based on different speed and load settings against the motor current feedback read by the inverter. The results have demonstrated that the platform is reliable and effective. In addition the monitoring method can be employed to detect and diagnose different degrees of misalignment in real time. This dissertation has major contributions to knowledge which includes: Understanding of real life machine condition monitoring problems for this application, including use of wireless sensor, communication over Industrial Ethernet and network security. The use of standard automation components (PLC and SCADA) in machine condition monitoring. MSc Research (Engineering) Thesis x An improved gearbox test rig platform which has the capability of remote control, acquiring and transferring data for monitoring induction machine drive system. The presented work shows that any machine using automated components such as PLC and SCADA and incorporating motor drive systems and other actuators has the potential to use the automated components for control, condition monitoring and reporting but this will require more tests to be done using the proposed platform

Investigation of a Rotating Shaft with a Novel Integrated Wireless Accelerometer

Rotating shafts are the most critical components of rotating machines such as motors, pumps, engines and turbines. Due to their heavy workloads, defects are more likely to develop during operation. There are many techniques used to monitor shaft defects by analysing the vibration of the shaft as well as the instantaneous angular speed (IAS) of the shaft. The signals are measured either using non-contact techniques such as laser-based measurement or indirect measurement such as the vibration on bearing housings. The advancement in low cost and low power Micro Electro Mechanical Systems (MEMS) make it possible to develop an integrated wireless sensor mounted on rotating shafts directly. This can make the fault diagnosis of rotating shafts more effective as it is likely to capture more details of shaft dynamics. This paper presents a novel integrated wireless accelerometer mounted directly on a rotating shaft and demonstrates that it can effectively monitor different degree of misalignments occurring commonly in a shaft system

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

A multi-sensor based online tool condition monitoring system for milling process

Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications

A Smart Modular Wireless System for Condition Monitoring Data Acquisition

Smart sensors, big data, the cloud and distributed data processing are some of the most interning changes in the way we collect, manage and treat data in recent years. These changes have not significantly influenced the common practices in condition monitoring for shipping. In part this is due to the reduced trust in data security, data ownership issues, lack of technological integration and obscurity of direct benefit. This paper presents a method of incorporating smart sensor techniques and distributed processing in data acquisition for condition monitoring to assist decision support for maintenance actions addressing these inhibitors

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0