Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> Need for Condition Monitoring and Maintenance Management in Industries. Cost Effective Benefits of Condition Monitoring. Condition Monitoring Techniques. Machinery Fault Diagnosis. Vibration Condition Monitoring. Condition Monitoring of Gear Systems. Condition Monitoring of Hydraulic Systems. Condition Monitoring of Pneumatic Systems. Condition Monitoring of Electrical Machinery. Condition Monitoring of Turbomachinery. Wear and Oil Debris Monitoring. Artificial Neural Networks in Condition Monitoring. Infrared Thermography in Condition Monitoring. Expert Systems in Condition Monitoring. Corrosion Monitoring. Environmental Monitoring - Legal Aspects. Cost Effective Benefits of Maintenance. Reliability Centred Maintenance. Total Productive Maintenance. Modern Maintenance Management Techniques. <s> BIB001 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> Research has picked up a fervent pace in the area of fault diagnosis of electrical machines. Like adjustable speed drives, fault prognosis has become almost indispensable. The manufacturers of these drives are now keen to include diagnostic features in the software to decrease machine down time and improve salability. Prodigious improvement in signal processing hardware and software has made this possible. Primarily, these techniques depend upon locating specific harmonic components in the line current, also known as motor current signature analysis (MCSA). These harmonic components are usually different for different types of faults. However with multiple faults or different varieties of drive schemes, MCSA can become an onerous task as different types of faults and time harmonics may end up generating similar signatures. Thus other signals such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. Human involvement in the actual fault detection decision making is slowly being replaced by automated tools such as expert systems, neural networks, fuzzy logic based systems to name a few. Keeping in mind the need for future research, this review paper describes different types of faults and the signatures they generate and their diagnostics' schemes. <s> BIB002 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> In recent years there has been an increasing interest in applying cyclostationary analysis to the diagnostics of machine vibration signals. This is because some machine signals, while being almost periodic, are not exactly phase-locked to shaft speeds, and thus even after compensation for speed fluctuation cannot be extracted by synchronous averaging. Typical examples are the combustion events in IC engines, which vary from cycle to cycle, and impulsive signals from faults in rolling element bearings, which are affected by minor but randomly varying slip. Two main tools for the analysis of cyclostationary signals are the two-dimensional autocorrelation function vs central time on the one axis and time displacement around the central time on the other, and its two-dimensional Fourier transform known as the spectral correlation. The latter can be quite complex to interpret, so some authors have suggested integrating it over all frequencies to obtain a Fourier series spectrumvs cyclic frequency. In this paper, it is shown that this gives the same result as a Fourier transform of the average squared envelope of the signal, which is much easier to obtain directly. Not only that, envelope analysis has long been used in the diagnostics of rolling element bearing signals, and some of the experience gained can be carried over to spectral correlation analysis. There is a possibility that the full spectral correlation may still give some advantage in distinguishing between modulation effects due to gear rotations and bearing inner race rotations (even at the same speed) by virtue of the different amounts of randomness associated with each. <s> BIB003 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> Condition monitoring of rotating electrical machinery has received intense research interest for more than 30 years. However, electrical machinery has been considered reliable and the application of fast-acting digital electrical protection has rather reduced the attention operators pay to the equipment. The area based upon current literature and the author's experience is reviewed. There are three restrictions: only on-line techniques for rotating machines are dealt with; specific problems of variable speed drives are not dealt with, except in passing; conventional rather than emerging brushless, reluctance and permanent magnet machines of unusual topology are concentrated upon. The art of condition monitoring is minimalist, to take minimum measurements from a machine necessary to extract a diagnosis, so that a condition can be rapidly inferred, giving a clear indication of incipient failure modes. The current state of the art is reviewed in the following ways: survey developments in condition monitoring of machines, mechanically and electrically, over the last 30 years; put that work in context alongside the known failure mechanisms; review those developments which have proved successful and identify areas of research which require attention in the future to advance the subject. <s> BIB004 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> This paper deals with small-power energy harvesting from heat. It can be achieved using both thermoelectric and pyroelectric effects. In the first case, temperature gradients are necessary. The main difficulty of thermoelectric energy harvesting is imposing a large temperature gradient. This requires huge heat flows because of the limited surface heat exchanges and the large heat conductivity of thermoelectric materials. This results in a drastic decrease of power and the efficiency of conversion. In case of pyroelectric energy harvesting, a time varying temperature is necessary. Although such a temperature time profile is hard to find, the overall optimization is easier than the thermoelectric strategy. Indeed, it depends much less on heat exchange between the sample and the outer medium, than on heat capacity that dimensions optimization may easily compensate. As a consequence, it is shown that the efficiency and output power may be much larger using pyroelectric energy harvesting than thermoelectric methods. For instance, using a limited temperature gradient due to the limited heat exchange, a maximum efficiency of 1.7% of Carnot efficiency can be expected using a thermoelectric module. On the contrary, a pyroelectric device may reach an efficiency up to 50% of Carnot efficiency. Finally, an illustration shows an estimation of the output power that could be expected from natural time variations of temperature of a wearable device. Power peaks up to 0. 2m W cm −3 were found and a mean power of 1 μ Wc m −3 on average was determined within 24 h testing. <s> BIB005 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> This paper proposes a novel industrial wireless sensor network (IWSN) for industrial machine condition monitoring and fault diagnosis. In this paper, the induction motor is taken as an example of monitored industrial equipment due to its wide use in industrial processes. Motor stator current and vibration signals are measured for further processing and analysis. On-sensor node feature extraction and on-sensor fault diagnosis using neural networks are then investigated to address the tension between the higher system requirements of IWSNs and the resource-constrained characteristics of sensor nodes. A two-step classifier fusion approach using Dempster-Shafer theory is also explored to increase diagnosis result quality. Four motor operating conditions-normal without load, normal with load, loose feet, and mass imbalance-are monitored to evaluate the proposed system. Experimental results show that, compared with raw data transmission, on-sensor fault diagnosis could reduce payload transmission data by 99%, decrease node energy consumption by 97%, and prolong node lifetime from 106 to 150 h, an increase of 43%. The final fault diagnosis results using the proposed classifier fusion approach give a result certainty of at least 97.5%. To leverage the advantages of on-sensor fault diagnosis, another system operating mode is explored, which only transmits the fault diagnosis result when a fault happens or at a fixed interval. For this mode, the node lifetime reaches 73 days if sensor nodes transmit diagnosis results once per hour. <s> BIB006 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future. ::: ::: Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments. <s> BIB007 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> This paper is intended to guide developers of wireless systems who are puzzled by the vast number of radio configuration parameters and options. We provide experimental data comparing power consumption of Bluetooth Low Energy (BLE), ZigBee and ANT protocols for a cyclic sleep scenario, in which a short-range and low-power wireless sensor node periodically sends a data packet to a remote `hub' with intervening sleep intervals. Devices such as wearable health monitors often use this scenario when interfacing with a mobile phone-based hub. For all measured sleep intervals BLE achieved lower power consumption (10.1 uA, 3.3 V supply at 120 s interval), compared with ZigBee (15.7 uA), and ANT (28.2 uA). Most of the power consumption differences can be attributed to the time taken for a node to connect to the hub after waking up and the use of sleep between individual RF packets. For the three protocols we determined a sleep interval at which the tradeoff between power consumption and data rate is optimized. <s> BIB008 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> Motor current signature analysis (MCSA) has been an effective way of monitoring electrical machines for many years. However, inadequate accuracy in diagnosing incipient broken rotor bars (BRB) has motivated many studies into improving this method. In this paper a modulation signal bispectrum (MSB) analysis is applied to motor currents from different broken bar cases and a new MSB based sideband estimator (MSB-SE) and sideband amplitude estimator are introduced for obtaining the amplitude at (1±2s)fs(1±2s)fs (s is the rotor slip and fsfs is the fundamental supply frequency) with high accuracy. As the MSB-SE has a good performance of noise suppression, the new estimator produces more accurate results in predicting the number of BRB, compared with conventional power spectrum analysis. Moreover, the paper has also developed an improved model for motor current signals under rotor fault conditions and an effective method to decouple the BRB current which interferes with that of speed oscillations associated with BRB. These provide theoretical supports for the new estimators and clarify the issues in using conventional bispectrum analysis. <s> BIB009 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to a base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments. <s> BIB010 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> Advancement in recent wireless technology has triggered the need for devices to run on independent power sources. This is evident especially with the wireless sensor network (WSN). This can be accomplished via harvesting energy from the surrounding environment such as solar, wind etc. These enery harvesting devices can power the wireless sensor nodes either directly of in conjunction with a battery. This paper presents the development of a solar energy harvesting mechanism for WirelessHART sensor node using photovoltaic (PV) cell array. Experimental results on the WirelessHART nodes proved that the developed harvester is capable of powering the nodes. <s> BIB011 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> This paper presents a state-of-the-art review on a hot topic in the literature, i.e., vibration based energy harvesting techniques, including theory, modelling methods and the realizations of the piezoelectric, electromagnetic and electrostatic approaches. To minimize the requirement of external power source and maintenance for electric devices such as wireless sensor networks, the energy harvesting technique based on vibrations has been a dynamic field of studying interest over past years. One important limitation of existing energy harvesting techniques is that the power output performance is seriously subject to the resonant frequencies of ambient vibrations, which are often random and broadband. To solve this problem, researchers have concentrated on developing efficient energy harvesters by adopting new materials and optimising the harvesting devices. Particularly, among these approaches, different types of energy harvesters have been designed with consideration of nonlinear characteristics so that the frequency bandwidth for effective energy harvesting of energy harvesters can be broadened. This paper reviews three main and important vibration-to-electricity conversion mechanisms, their design theory or methods and potential applications in the literature. As one of important factors to estimate the power output performance, the energy conversion efficiency of different conversion mechanisms is also summarised. Finally, the challenging issues based on the existing methods and future requirement of energy harvesting are discussed. <s> BIB012 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> An industrial revolution named “Internet of Things” has already started with the number of devices connected to the Internet outnumbering the number of humans. A number of application domains (like smart healthcare, smart home, smart agriculture, smart transportation, etc.) are being researched with new domains springing up. However, a number of wireless connectivity protocols (including Bluetooth, Bluetooth low energy, ZigBee, Z-wave, Wi-Fi, 6LowPAN, GSM, UWB, etc.) can be used in IoT applications. There is therefore the challenge of how to select the most efficient wireless connectivity standard based on data rate, network range, type of devices, environment, distance, energy consumption, etc. Hence, this work tests and compares three commonly used wireless connectivity modules, namely, Bluetooth, ZigBee and Wi-Fi based on power consumption, range, topology and cost in order to provide IoT application developers with communication interface paradigm for sensors and actuators in the cloud of IoT. A user-friendly mobile application (Blynk) is used to monitor and control connected devices over the Internet. <s> BIB013 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> Abstract This paper deals with a development cycle and virtual prototyping of energy harvesting systems for industrial applications. Presented energy harvesting systems are an interesting alternative for autonomous powering of ultra-low power electronics, wireless sensors, condition reporting systems and structural monitoring systems. Energy harvesting systems could be able to convert an ambient energy to electricity and thus provide an autonomous power supply for mentioned applications without the need for batteries or wiring. Mechanical and thermal energy are promising autonomous sources of electricity mainly in aeronautics. Due to the recent development of the ultra-low power electronics wireless sensors they could be also embedded within machines, buildings and structures. The automatic collection of the measured information will be forwarded for the monitoring system. The power consumption of modern electronics is dropping, and the energy harvesting sources are starting to be a feasible energy solution in specific industrial applications. However, the amount of harvested energy is still quite small. Regarding that fact, the output electrical power must be predicted and compared with the power requirements of the intended electronics. This paper presents useful simulation tools for the prediction of harvested power under industrial operation, which consist of mathematical and simulation models of such multidisciplinary devices and experimental input data of ambient energy. The designed virtual prototypes include mathematical models of energy conversion, physical laws, multi-physical feedbacks, geometry, electronics, etc. Such virtual models are necessary for the development of new autonomous systems for industrial applications. Otherwise energy harvesting systems based on new smart materials and structures that could be integrated in the presented models for the future development of IoT applications. <s> BIB014 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Introduction <s> The operational efficiency of remote environmental wireless sensor networks (EWSNs) has improved tremendously with the advent of Internet of Things (IoT) technologies over the past few years. EWSNs require elaborate device composition and advanced control to attain long-term operation with minimal maintenance. This article is focused on power supplies that provide energy to run the wireless sensor nodes in environmental applications. In this context, EWSNs have two distinct features that set them apart from monitoring systems in other application domains. They are often deployed in remote areas, preventing the use of mains power and precluding regular visits to exchange batteries. At the same time, their surroundings usually provide opportunities to harvest ambient energy and use it to (partially) power the sensor nodes. This review provides a comprehensive account of energy harvesting sources, energy storage devices, and corresponding topologies of energy harvesting systems, focusing on studies published within the last 10 years. Current trends and future directions in these areas are also covered. <s> BIB015
Condition monitoring is a process of judging the health status of a mechanical system, which uses various types of data (such as temperature, vibration, strain, rotating speed, displacement, pressure, voltage, current, acoustics and operator experience) to achieve change-point detection and thus provide a timely decision for the maintenance works BIB001 . Machine condition monitoring delivers significant benefits of cost savings, safety and reliability to industries by providing an early indication of potential machine failure in the machine operation cycle. As such, condition monitoring has attracted substantial attention from companies and research institutions for decades BIB002 BIB004 BIB006 . Traditionally, plenty of wires or cables are required in a condition monitoring system to transfer data from various transducers to data acquisition devices. High costs and difficult installations, along with low operational reliability are As shown in Figure 1 , wasted energy sources (such as light, electromagnetic radiation, heat, vibration, motion and magnetic energy) can be harvested using various traditional EH techniques (usually including photovoltaic BIB011 , radio frequency (RF), thermoelectric, pyroelectric BIB005 , piezoelectric, electromagnetic, triboelectric and electrostatic BIB012 BIB014 ). Currently, these EH techniques are primarily targeted at small and ultra-low power devices, like portable electronic devices, wearable devices and WSNs BIB015 . For mechanical systems, the energy losses are present in power transformation and transmission in the form of friction, heat, deformation and vibration during operation. Therefore, it is recognized that mechanical efficiency is always below 100%. Besides, more efficient, renewable and generally inexhaustible energy sources are likely to exist in the environment around the mechanical systems. As shown in Figure 1 , wasted energy sources (such as light, electromagnetic radiation, heat, vibration, motion and magnetic energy) can be harvested using various traditional EH techniques (usually including photovoltaic BIB011 , radio frequency (RF), thermoelectric, pyroelectric BIB005 , piezoelectric, electromagnetic, triboelectric and electrostatic BIB012 BIB014 ). Currently, these EH techniques are primarily targeted at small and ultra-low power devices, like portable electronic devices, wearable devices and WSNs BIB015 . For mechanical systems, the energy losses are present in power transformation and transmission in the form of friction, heat, deformation and vibration during operation. Therefore, it is recognized that mechanical efficiency is always below 100%. Besides, more efficient, renewable and generally inexhaustible energy sources are likely to exist in the environment around the mechanical systems. Low power consumption, low cost, high security and low latency. ZigBee   BIB007 BIB008 BIB013 250 k 10 to 100 m 4 72 84 Low power consumption, low cost, low complexity and self-organization. EnOcean BIB010 RF-based, low cost, low power consumption, low radiation, anti-interference and high reliability. ANT BIB007 BIB008 60 k 30 m at 0 dBm 3 110 75 Utra-low power consumption, high flexibility and proprietary. Currently, there are a number of popular modules available for developing WSN applications. As summarized in Table 2 , these modules are fabricated based on the wireless communication technologies in Table 1 . However, due to differences in applications and advances in fabrication technologies used, they exhibit different performances of energy usages even if they are from the same communication technology. It shows that the energy consumed is generally at the level of milliwatts or watts. Both Wi-Fi and BLE are the most energy efficient transmission methods by evaluation of data rate and power consumption, which have the potential to be competitors of other wireless communication technologies for the achievement of remote and real-time machine condition monitoring taking into account of their relatively long transmission distance and high transmission rate. Please refer to the datasheets for various modules. The power consumed by processing and sensing units is used for data collection and data processing. Table 3 enumerates typical microprocessor modules with active consumption at about watt level. In reality, the electricity cost by a microprocessor is dependent up on the instructions processed. In other words, the implementation of massive complex algorithms results in a significant increase in power consumption of a sensor node microprocessor, for example, from dozens of milliwatts or hundreds of milliwatts. Please refer to the datasheets for various modules. In contrast, the sensing unit consumes the least power, generally at the microwatt or milliwatt level, which can be certified from the parameters of sensor modules as shown in Table 4 . It is also deduced that the transmission unit of a wireless sensor node consumes the most energy causing power consumption of an entire node to be at the milliwatt to watt level, the comparison of various modules are shown in Tables 2-4. The main physical parameters measured for machine condition monitoring include temperature, humidity, torque, stress, strain, pressure, speed, vibration and acoustic emission. The lower varying signals, such as temperature are suggested for a network with a low transmission rate and power consumption, typically ZigBee, EnOcean, Z-wave and ANT. However, it is difficult to determine the early fault diagnosis and fault location using such limited data. The vibrational characteristics of the mechanical faults are usually found at a much higher frequency band, which requires a high sampling frequency according to the Nyquist-Shannon sampling theorem, resulting in a large number of raw datasets for processing and transmission. Although feature extraction at nodes consumes less power than the direct transmission of raw datasets, some advanced algorithms for extracting fault features with high computational costs, for example, for example Spectral Correlation BIB003 and Modulation Signal Bispectrum  BIB009 , cannot be operated at nodes. Therefore, it is still essential to transmit large numbers of raw datasets through a WSN with a high transmission speed to implement the remote and real-time machine condition monitoring. The relationship between power consumption and transmission rate has been researched, unveiling that the long-term data transmission at a low rate can consume more power than a short-term transmission at a high rate when the effect of transmission distance is excluded   . Therefore, a wireless network with a higher transmission rate (for example BLE, Wi-Fi and ZigBee) is critical in order to achieve remote machine condition monitoring in real time. BLE 4.2 consumes less power, but the actual transmission distance is limited to 10 m. It is attractive that BLE 5.0, a new generation of Bluetooth, has a wider coverage range and a higher speed compared to BLE 4.2 for low-power devices. This provides an opportunity to achieve a large amount of datasets transmission in real time with relatively low power consumption in online machine condition monitoring. The improvement of intelligent networks and big data analytics provides another solution for self-powered machine condition monitoring systems. MCUs, such as the ARM Cortex-M series, have vast processing capacity with the power consumption levels for data processing being substantially less than the power used for data transmission. As a result, a large amount of sampled data can be partially pre-processed in the MCU, and then the signal features are extracted and transmitted instead of transmitting the full raw data which provides significant power saving. In general, a trade-off between the network selection and energy harvesting should be considered before a wireless condition monitoring system is implemented. Moreover, there should be a balance between data processing and data transmission in algorithm design and network optimization. In general, power at the milliwatt level or watt level is required to process and transmit large amounts of rapidly varying signals at a wireless sensor node for remote and real-time machine condition monitoring.