A technical framework and roadmap of embedded

314 IEEE TRANSACTIONS ON RELIABILITY, VOL. 61, NO. 2, JUNE 2012

A Technical Framework and Roadmap of EmbeddedDiagnostics and Prognostics for ComplexMechanical Systems in Prognostics and

Health Management SystemsZ. S Chen, Y. M Yang, and Zheng Hu

Abstract—Prognostics and Health Management (PHM) tech-nologies have emerged as a key enabler to provide early indicationsof system faults and perform predictive maintenance actions. Im-plementation of a PHM system depends on accurately acquiringin real time the present and estimated future health states of asystem. For electronic systems, built-in-test (BIT) makes it notdifficult to achieve these goals. However, reliable prognosticscapability is still a bottle-neck problem for mechanical systemsdue to a lack of proper on-line sensors. Recent advancements insensors and microelectronics technologies have brought abouta novel way out for complex mechanical systems, which is calledembedded diagnostics and prognostics (ED/EP). ED/EP can pro-vide real-time present condition information and future healthstates by integrating micro-sensors into mechanical structureswhen designing and manufacturing, so ED/EP has a revolutionaryprogress compared to traditional mechanical fault diagnostic andprognostic ways. But how to study ED/EP for complex mechanicalsystems has not been focused so far. This paper explores thechallenges and needs of efforts to implement ED/EP technolo-gies. In particular, this paper presents a technical frameworkand roadmap of ED/EP for complex mechanical systems. Theframework is based on the methodology of system integrationand parallel design, which includes six key elements (embeddedsensors, embedded sensing design, embedded sensors placement,embedded signals transmission, ED/EP algorithms, and embeddedself-power). Relationships among these key elements are outlined,and they should be considered simultaneously when designing acomplex mechanical system. Technical challenges of each key ele-ment are emphasized, and the corresponding existed or potentialsolutions are summarized in detail. Then a suggested roadmapof ED/EP for complex mechanical systems is brought forwardaccording to potential advancements in related areas, which canbe divided into three different stages: embedded diagnostics,embedded prognostics, and system integration. In the end, thepresented framework is exemplified with a gearbox.

Index Terms—Complexmechanical systems, embedded diagnos-tics and prognostics, prognostics and health management.

Manuscript received October 15, 2011; revised December 26, 2011; acceptedJanuary 20, 2012. Date of publicationMay 17, 2012; date of current versionMay28, 2012. This work was supported by the National Natural Science Foundationof China under Grant 50805142. Guest Associate Editor: Q. Miao.The authors are with the Key Laboratory of Science and Technology on ILS,

College of Mechatronics Engineering and Automation, National University ofDefense Technology, Changsha 410073, China (e-mail: czs_study@ sina.com;[email protected]; [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TR.2012.2196171

ACRONYMS

ED/EP embedded diagnostics and prognostics

PHM prognostics and health management

CBM condition based maintenance

RUL remaining useful life

BIT built-in-test

SNR signal-to-noise ratio

QSDG quantitative signed directed graph

EH energy harvesting

I. INTRODUCTION

P ROGNOSTICS AND HEALTH MANAGEMENT(PHM) comes from the U.S. Department of Energy

and the U.S. Department of Defense (DoD). The motivationis to reduce the operation and support (O&S) costs of largemilitary or industrial systems while maintaining or increasingthe availability of these systems. According to [1]–[5], PHM isan approach to system life-cycle support that seeks to reduceor eliminate inspections and time-based maintenance throughaccurate monitoring, incipient fault detection, and predictionof impending faults. So PHM technologies have emerged as akey enabler to provide early indications of system faults, andperform predictive maintenance actions. The capability allowsend users to perform better planned maintenance, reduce oreliminate unnecessary inspections, and decrease time-basedmaintenance intervals with confidence. When coupled withautonomic logistics, PHM can improve mission-critical systemreliability & availability, and reduce logistics delay time,on-demand repair actions and sparing, and life-cycle costs [2],[6]–[8].By now, many studies have been done for PHM as a means of

providing advancedwarnings of failure, and enabling condition-based maintenance. DARPA is interested in developing “self-aware” systems [9]. The Applied Research Laboratory at Penn-sylvania State University has been working in the area of con-dition-based maintenance and machinery health monitoring formany years. They have developed many tools and approachesrelating to PHM [10]. The Society for Machinery Failure Pre-vention Technology (MFPT) holds an annual meeting on PHMeach year. Prof. Michael G. Pecht at the University of Maryland

0018-9529/$31.00 © 2012 IEEE

CHEN et al.: FRAMEWORK AND ROADMAP OF ED/EP FOR COMPLEX MECHANICAL SYSTEMS IN PHM SYSTEMS 315

has conducted many valuable studies on PHM [11]–[13], andproposed a PHM roadmap for information and electronics-richsystems [13]. Impact Technologies, Inc. has developed engi-neering tools and generic customized software modules for me-chanical component diagnostics and prognostics over a broadrange of applications [14]. These software modules can be tai-lored for both critical, high fidelity applications such as mil-itary aircraft subsystems, and lower cost, higher volume in-dustrial applications. The Machinery Information ManagementOpen Systems Alliance (MIMOSA) has adopted the develop-ment and support of the Open System Architecture ConditionBased Maintenance (OSA-CBM) standard that purports to pro-vide a standard architecture for PHM systems [15].Based on the OSA-CBM structure, a PHM system can be di-

vided into several layers: sensors & data acquisition, conditionmonitoring, fault diagnostics, predicting remaining useful life(RUL), and health management. The PHM system generallycombines sensing and interpretation of environmental, opera-tional, and performance-related parameters to assess the healthof a product, and predict its remaining useful life. Thus, in PHMsystems, it is the most important to obtain real-time conditioninformation of a given subsystem accurately, which is the basisof fault detection, and predicting its future health status. PHMrelies on long-term accurate in situ information. For electronicsystems, built-in-test (BIT) makes it easy to achieve these goals[16], [17]. BIT can incorporate test and diagnostic functionsinto an electronic component at the design stage. Such a designphilosophy has been widely applied to the design and testingof complex electronic systems, such as integrated circuits. Onthe other hand, mechanical systems play increasingly importantroles in industry, aeronautics, military areas, and so on. Further-more, mechanical systems are significantly contributed to bothsafety incidents and maintenance costs. So it is necessary toperform fault prognostics and health management of complexmechanical subsystems for preventing major breakdowns dueto progression of undetected faults. Unlike electronic systems,the condition of a complex mechanical system is always char-acterized by physical signals which are stochastic and prone tobe contaminated by surrounding noises, such as vibration [18],[19], temperature [20], and so on. Also, there are frequently notesting points reserved on key components in mechanical sys-tems at the design stage. We can only mount sensors outside, soit is very difficult to acquire ‘true’ condition signals of thesecomponents. Consequently, there is a pressing need to applyBIT philosophy into complexmechanical systems for PHM.Re-cent advancements in sensors and micro-electronics technolo-gies have brought about a novel way of BIT applications forcomplex mechanical subsystems, which is called Embedded Di-agnostics and Prognostics (ED/EP) [21], [22].Over the past decade, ED/EP has been considered to be a key

component of PHM systems. Research in the ED/EP of mechan-ical systems or structures is a means of providing on-line con-dition information, and enabling condition-based maintenance.R.X. Gao embedded a wireless sensor module into a small slotmilled on the outer raceway of a ball bearing to provide “on-the-spot” fault detection capability [23]. Acellent <<AQ1>>, Inc.has developed a patented SMART Layer technology for healthmonitoring of structures, which is a thin dielectric film with anembedded piezoelectric sensor network that can be embedded

inside composite structures [24]. Impact Technologies, Inc. pro-vides a capability to deploy the most advanced embedded healthmonitors using a variety of methods from stochastic processes tostate space models and artificial intelligence [25]. TEAMS-RTof Qualtech Systems, Inc. is a fast, compact reasoner running ona vehicle’s on-board computer to provide real-time diagnosticsand system health monitoring. It uses a subset of the TEAMSmodel of the vehicle or machine, and processes the results ofon-board, built-in tests to perform diagnostics [26].However, to our knowledge, there are no systematic guide-

lines for studies on the ED/EP of mechanical systems. The goalof this paper is to present a technical framework of ED/EP forcomplex mechanical systems, and underlying opportunities &challenges. Then a roadmap of ED/EP is suggested to show itsfuture trends. In the end, we present an example to demonstratethe implementation of ED/EP.

II. DEFINITION OF ED/EP, AND ITS NEEDS FOR COMPLEXMECHANICAL SYSTEMS

A. Roles of ED/EP in PHM Systems

The importance of PHM has been explicitly stated in the U.S.Department of Defense (DoD) 5000.2 policy document on de-fense acquisition. A definition of PHM proposed by Sandia Na-tional Laboratories (SNL) is as ‘the capability to estimate thelikelihood of a system failure over some future time interval sothat appropriate actions can be taken’ [27]. In general, a PHMsystem consists of several features:• Raw Data—On-line, off-line sensor data—Historical maintenance/failure data—Model data

• Diagnostics—Data pre-processing—Data interpretation—Data fusion—Determining fault types and severities

• Prognostics— Estimation of system “health” index— Predictions of RULs

• Health Management—What should be done?—How to do it?—When should it be done?Also SNL has proposed a PHM system structure as Fig. 1[27]. The Evidence Engine aims for feature extraction,trend detection, information fusion, and estimation of re-maining useful life. And the Consequence Engine aims toanalyse the consequences of various maintenance actions.It can be seen from the PHM structure that sensor subsys-tems are the basis of PHM. In particular, PHM systemsgive rise to special needs on sensor subsystems with mul-tiple sensing abilities, miniature size and light weight, lowpower consumption, long range and high rate data trans-mission, large onboard memory, fast onboard data pro-cessing, low cost, and high reliability [28].The typical structure of sensor modules for PHM is shownin Fig. 2. It can be seen that onboard sensing and processing


Fig. 1. The PHM system structure [27].

Fig. 2. Typical structure of sensor modules for PHM systems.

functions are integrated into the sensor modules. In tradi-tional sensing strategies, we often need to mount sensorson structures, and then collect the signals by one acquisi-tion card, so that miniature size and low power consump-tion cannot be achieved.The U.S. DoD points out that the manager in PHM systemscan optimize operational readiness through affordable, in-tegrated ED/EP, embedded training and testing, serializeditem management, automatic identification technology,and iterative technology refreshment [29]. So ED/EP hasbeen considered to be a key enabler to PHM.There have been no standard definitions for ED/EP in theliterature. In this paper, we define ED/EP as ‘analyzing datafrom embedded sensors or test equipment to continuouslyassess the operational status of a device, and predict itsfuture state. When the performance or predicted remaininglifetime reaches a specified threshold, it will issue an alert,and submit a requisition for maintenance’.

B. ED/EP of Complex Mechanical Systems

During the life cycle of a complex mechanical system, the de-sign stage is often separated from its usage stage. That is to say,the designers rarely consider how to reserve testing points andperform health monitoring. Thus in practice it is necessary tomount additional sensors to perform condition monitoring, faultdiagnostics, and prognostics. For example, vibration analysis isthe most popular way of condition monitoring and fault detec-tion for complex mechanical systems. By observing the changesin the vibration spectra over time, a skilled engineering can in-terpret its condition, and more importantly give prior warning

Fig. 3. Comparison between (a) traditional methods and (b) ED/EP.

of imminent failure. This warning facilitates a planned mainte-nance and repair program, and avoids costly down-time. A tra-ditional fault diagnostic and prognostic scheme of mechanicalsystems is shown in Fig. 3(a).In practice, however, it is very difficult to achieve accurate

fault diagnostics and prognostics using traditional methods forcomplex mechanical systems such as helicopter gearboxes, andaero engines. The reasons may include the following.• Sensors may not access a faulty component directly dueto many limitations. Because there are no reserved testingpoints at the design stage, we can only mount sensors onmechanical components or structures to sample signals.However, in many cases, we cannot easily mount a sensoron a faulty component directly, such as bearings or gearsin a helicopter gearbox, shafts in an engine, and so on. Inparticular, it may not be allowed to mount sensors on com-pact or moving structures in mechanical systems.

• Low signal-to-noise ratio (SNR) due to surrounding in-terferes. For vibration analysis, the monitored mechanicalcomponent cannot be sensed directly in many cases, so vi-bration signals from sensors are always not the ‘true’ con-dition signal, but mixed with signals from other compo-nents and environment noise. Thus SNRs of useful condi-tion signals are low.

• Difficult to extract accurate fault features. Because me-chanical systems are often complex and work under vari-able loads, its dynamic behaviors are also complex dueto strong coupling among different components. Thus itis difficult to extract accurate fault features by traditionalways.

• Poor prognostic ability. There are often no on-line sensorson key components in mechanical systems, and we cannotobtain continuous condition signals, so the ability of faultprognostics is very poor.

Up to now, real-time and accurate signal acquisition hasbeen a bottle-neck problem for complex mechanical systems.To solve this problem, the concept of ED/EP has been proposedby the U.S. Army. The basic scheme of ED/EP is shown in


Fig. 4. A technical framework of ED/EP for complex mechanical systems.

Fig. 3(b), where the capability of fault diagnostics and prog-nostics is considered in parallel at the design stage.The features of ED/EP of complex mechanical systems are

numerous:• Bit-in-Test (BIT) and prognostic functions for CBM/PHMsystems are possible.

• Micro-sensors integrated into mechanical components orstructures at the design stage, which makes mechanicalsystems self-sensing, self-diagnostic and prognostic, andself-reporting.

• Near-real-time fault diagnostics and prognostics abilitywith less human interaction.

• Smart, autonomous mechanical systems are possible.

III. A TECHNICAL FRAMEWORK OF ED/EP FOR COMPLEXMECHANICAL SYSTEMS

Developing the infrastructure to implement an ED/EP systemis still a major challenge, and underlying key technologies haveto be outlined. In this section, a technical framework of ED/EPfor complex mechanical systems is proposed in Fig. 4, which in-cludes six key elements: embedded sensor modules, embeddedsensing design, optimal placement of embedded sensors, em-bedded signal transmission, ED/EP algorithms, and embeddedself-power.The arrows denote the restriction relationships among these

elements, which can be summarized as follows.• When designing embedded sensor modules, we must con-sider the feasibility of signal transmission under embeddedworking conditions, implementation of ED/EP algorithmsunder the limited calculation sources of micro-processors,space limitations in complex mechanical systems, andpower consumption limitations for long lifetimes.

• When performing embedded sensing design, we must con-sider the restrictions of the size of each embedded sensormodule, and allowable spaces in complex mechanicalsystems.

• When performing embedded sensors’ optimal placement,we must consider the restrictions of the size of each em-bedded sensor module, allowable spaces, costs, and theability of diagnostics and prognostics.

Also, there are two important integration procedures in Fig. 4.The first one is the integration of embedded miniature sensormodules by itself, which includes the sensing element, signal

transmission element, self-powering element, and signal pro-cessing algorithms. The other is the integration between em-bedded miniature sensor modules and mechanical componentsor structures.The main challenges of ED/EP that we face are numerous.• What kind of key condition parameters must bemonitored?• What kind of sensor modules should be used?• Miniature, robust, reliable sensor modules are needed forembedding.

• Where should sensor modules be embedded?• How do we embed sensor modules to reduce their effectson the structure integrity?

• How do we embed the minimum number of sensors tocover the maximum number of faults, i.e., optimal place-ment?

• What are the costs and benefits associated with embeddingsensor modules?

• Reliable signal transmission is necessary.• The calculation abilities and storage space of microproces-sors are limited.

• Embedded sensor modules must be self powered.

A. Embedded Sensor Modules for Mechanical Systems

ED/EP relies highly on the sensor systems to obtainlong-term, accurate, in situ information to provide anomalydetection, fault isolation, and rapid failure prediction. Em-bedded sensor modules are the essential devices used tomonitor parameters for ED/EP of mechanical systems, such astemperature, vibration, shock, pressure, acoustic levels, strain,stress, and so on. Considerations of embedded sensor modules’design for ED/EP may include the parameters to be measured,the performance needs, multiple sensing abilities, miniaturesize and light weight, low power consumption, large onboardmemory, fast onboard data processing, low cost, and highreliability. A generic embedded sensor module will typicallyhave sensing elements, onboard analog-to-digital converters,onboard memory, embedded computational capabilities, datatransmission, and a power source or supply [22]. In recentyears, significant growth in the field of adaptive structureshas achieved advancement in sensor material that can performembedded measurement. Chief among these are piezoelectric,magnetstrictive, and optical materials. By now, the promisingembedded sensors mainly include acoustic wave sensors,electromagnetic sensors, optical sensors, Micro-Electro-Me-chanical Systems (MEMS) sensors, and thin film sensors [30].

B. Embedded Signal Transmission for Mechanical Systems

For the ED/EP of complex mechanical systems, reliablesignal transmission is a challenge. Traditional hard wiringmethods will face many problems because wiring may notbe permitted in some extreme environments, or many em-bedded sensors will push the weight and cost of wiring tosignificant levels. There are two main embedded signal trans-mission methods for complex mechanical systems: optical fibercommunication, and wireless communication [30]. Opticalcommunication has been researched extensively over the pastten years for different applications, which has the potential forimplementing multiple sensors on a single fiber. For example,optical sensors can be embedded into the composite structures


of aircrafts to transmit strain, and temperature signals. Wirelesscommunication has advanced significantly in commercialpractice, and the emphasis is on high channel bandwidth andlow power for ED/EP. There are several mature wirelesscommunication protocols, such as 802.11b, Bluetooth, RF,and so on. Because many mechanical systems are made ofmetal material, electromagnetic interferences have unavoidableimpacts on the performance of wireless communication. Thismatter must be addressed to permit reliable wireless signaltransmission as the RF environment becomes increasinglycrowded in complex mechanical systems. Utilization of codingschemes, frequency management, management of transmittedpower, and directional antennas may factor into controllingand adapting to changing RF conditions. Future research willfocus on ultra-width band, long range, and high rate datatransmission.

C. Embedded Sensing Design for Mechanical Systems

The implementation of such an ED/EP mechanism needs toembed miniature sensor modules into mechanical componentsor structures, such as a bearing or a gear. However, how toembed the miniature sensor modules into mechanical systemsis a challenging task because it indeed will affect the structuralintegrity. To ensure consistent structural integrity after embed-ding, it is necessary to perform embedded sensing design. Thereare two important steps. First, dynamic analysis of a modifiedmechanical component or structure needs to be done to evaluatethe impact of embedded sensors on the structure integrity. Ingeneral, we can compare natural frequencies and modal shapes.Second, a finite element-based method is used for modeling andanalysis of the modified mechanical component or structure totestify the feasibility of embedded sensing design.

D. Embedded Sensors Optimal Placement for MechanicalSystems

For complex mechanical systems, embedding a single sensoris not enough for accurate fault diagnostics and prognostics.Obviously, a multi-sensor system can overcome the limitationsof a single sensor system, and has better performance. At thesame time, to avoid destructing the structural integrity, weoften prefer the number of sensors to be as small as possible.So we need to perform embedded sensors optimal placement.The goal is to obtain the maximum fault coverage with theminimum number of embedded sensors. We can build anoptimal model of embedded sensors placement by quantifyingfault sensitivities of all embedded sensors. For example, invibration analysis, the fault sensitivity of each embedded sensoris tightly related to transmission paths between its locationand fault source. So vibration propagation information canbe used for embedded sensors optimal placement. A signeddirected graph (SDG) method can provide a simple graphicalrepresentation of a complex system, and depict cause-effectrelations of fault vibration propagation [31]. Thus we can use aSDG of fault vibration propagation to build an optimal modelof embedded sensors placement. The corresponding optimalalgorithms include genetic algorithm (GA) [32], particle swarmoptimization (PSO) algorithm [33], and so on.

E. ED/EP Algorithms for Mechanical Systems

The core of ED/EP is a microprocessor, so its calculation andstorage ability are limited. Many advanced signal processingor reasoning algorithms cannot be used here. To solve thisproblem, a model-based algorithm may be a proper alternative[34], such as AR, ARMA, neural network, and so on.

F. Self Power for ED/EP for Mechanical Systems

It is a major challenge to provide power for ED/EP systems.Traditionally, batteries are often used for wireless sensors. How-ever, the lifetime of a battery is always limited, so it must bereplaced periodically. When there are many embedded sensormodules, replacement of depleted batteries becomes time con-suming and significantly wasteful. In particular, it may not beprudent to replace the battery in some extreme conditions. Asa result, there is a clear need to explore novel alternatives topower embedded sensor modules. Harvesting energy from theirlocal environment has become a novel power source for wirelesssensors with potentially lower cost and weight, such as vibration[35], RF [36], thermal [37], and solar [38]. An embedded sensormodule will benefit from an extended life and from being selfpowered. Self power for embedded sensor modules can be splitinto two main technology categories: reducing the power con-sumption, and increasing the efficiency of energy harvesting.

IV. A SUGGESTED ROADMAP OF ED/EP FOR COMPLEXMECHANICAL SYSTEMS

The ED/EP of complex mechanical systems is far from ma-turity due to many technical challenges. However, potential ad-vancements in related areas will impel its development in thefuture, such as MEMS, advanced microelectronics, smart ma-terials and structures, advanced manufacturing, and so on. Inthis section, we suggest a roadmap of ED/EP for complex me-chanical systems shown in Table I, which can be divided intothree developing stages:1) Embedded diagnostic stage: In this stage, we need to focuson advanced embedded sensors under extreme conditions,on-line signal processing methods, on-line fault diagnos-tics and isolation algorithms, and reliable embedded signaltransmission technologies. By now, this stage has almostbeen accomplished by the U.S. Army.

2) Embedded prognostic stage: In this stage, we need to focuson reliable self-powered technologies, physics-of-failure(PoF) models-based fault prognostic methods, the fusionof data-driven and PoF-based prognostic algorithms, anddeveloping and testing the prototype of ED/EP for complexmechanical systems.

3) Integration stage: In this stage, the integration of ED/EP inmechanical systems is achieved. Complex mechanical sys-tems will be truly smart and autonomous, with the abilityof self-sensing, self-diagnostic and prognostic, self-report,even self-healing. So that the ED/EP of complex mechan-ical systems can be seamlessly connected to health man-agement information systems.

V. A CASE STUDY: ED/EP OF A GEARBOX

To demonstrate the feasibility of the proposed ED/EP struc-ture, a testing case is shown here. As we all know, gearboxesare one kind of important rotating machinery, and widely used


TABLE IA SUGGESTED ROADMAP OF ED/EP

Fig. 5. (a) A two-stage gearbox and (b) its internal view.

in military and industrial equipment. Therefore, the ED/EP ofgearboxes is necessary to prevent major breakdowns, and im-prove their operational capability.In this section, we choose a two-stage gearbox (shown

in Fig. 5) as an object to show the implementation of thetechnical framework in Fig. 4. Here, on-line vibration signalanalysis is used. The selected gearbox includes six bearings

and four gears as shownin Fig. 5(b).

A. Embedded Sensing Design

Bearing and gears are two kinds of key components in agearbox, which have different faults due to their work condi-tions. We can decide to embed miniature sensor modules intobearings and gears at the design stage. For bearings, a slot canbe cut from the outer race to hold the sensor module, as inFig. 6(a). At the same time, the slot must be kept small enoughto avoid causing the outer race to fail. Cutting a slot will causean increase in both stress and deflection, and deflection is thekey limiting factor for its structural integrity [39]. Then a finiteelement-based simulation can be done by changing slot sizes(length, width, and depth) to analyse their effects on deflection.

Fig. 6. (a) Modified bearing and (b) its percentage increase in radial deflection.

Fig. 7. (a) Schematic diagraph of a modified gear and (b) its finite elementmodel.

The results are shown in Fig. 6(b). In this way, the maximumslot size (length, width, and depth) can be determined.On the other hand, the degrees to which sampled vibration

signals are sensitive to faults are quite different at different lo-cations, so the location of a slot surrounding the outer race alsoneeds to be optimized when assembling. Studies have pointedout that the maximum sensitivity can be reached when thesensor locates at the maximum radial force point surroundingthe outer race [23], [39], [40]. For a simple bearing under onlyperpendicular load, obviously the optimal location of the slot isunderneath the bearing outer race. However, it is not the casein a gearbox because the maximum force may not be vertical tothe ground. There may be an optimal angle departing from thevertical axis. Furthermore, the optimal angle for each bearingcan be calculated based on forces analysis in the gearbox.For gears, we can embed a miniature sensor module by

drilling a hole, as in Fig. 7(a). Vibration signals can be trans-mitted to a data receiver wirelessly by a looped antenna.Similarly, we need to determine the diameter and depth of thehole, and its distance from the center of the gear so that drillingsuch a hole just causes an acceptable effect on its structureintegrity. Here, natural frequencies and modal shapes of agear are two important factors to be considered to measure theimpact of a hole. Finite element-based modal analysis can beused to determine the optimal location and size of a hole in thegear (Fig. 7(b)). In addition, symmetric holes will be drilled toavoid unbalance in practice.

B. Embedded Sensor Modules Design and Wireless SignalTransmission

Functions of a miniature sensor module may include col-lecting, processing, and transmitting signals wirelessly. Its per-


Fig. 8. The prototype of a miniature sensor module [43].

formances have played an important role in ED/EP systems.Here the miniature sensor module is designed by selecting com-ponents carefully from commercial-off-shelf (COTS) products.Two key principles must be considered: lower power consump-tion, and miniature size.In this case, one three-axial MEMS sensor named

LIS3L02AL produced by ST Corporation is used [41].LIS3L02AL is a low-power 3-axis linear capacitive accelerom-eter in a 36-pin QFN package, and its size is 6 mm 6 mm 1mm. The radio transceiver chip is chosen as nRF24E1 producedby Nordic Corporation [42], which is a 2.4 GHz RF transceiverin a 36 pin QFN package, and its size is 6 mm 6 mm 1 mm.When nRF24E1 enters a power down mode, its supply currentis only 2 A. The advantage of using nRF24E1 is that it needsother minimal components to complete the hardware design,so that a miniature size can be reached. Finally, the prototypeof a miniature sensor module is shown in Fig. 8, and its wholedimension is 9 mm 34 mm 3 mm [43]. A cellular batterymodel CR2032 can be used to power the sensor module.

C. Optimal Placement of Embedded Sensor Modules

The goal of embedded sensor optimal placement is to embedthe minimum number of sensors to cover the maximum numberof faults in the gearbox. To improve fault sensitivity, goodquality signals with high SNRs are needed, which are tightlyrelated to propagation paths between embedded sensors andfault locations. So fault vibration propagation information isused for embedded sensor optimal placement here.We consider six bearings and four gears as ten candidate lo-

cations of embedded sensor modules. Meanwhile, each locationrepresents a potential fault source. Then fault vibration propa-gation paths can be represented by a signed directed graph asin Fig. 9 [44], where each node represents one component ofthe gearbox ( , , , , , -bearing; , , , -gear).Each connection between two nodes denotes the interface, andan arrow denotes the direction of vibration propagation. Using“ ” or “ ” for each connection represents signal enhancementor decrement. It must be noted that this QSDG representationonly depicts dominant propagation paths in the gearbox ob-tained by finite element analysis. Each connection is quanti-fied by two weighted values, i.e., travel time, and attenuationof vibration. These two weights can be used to define fault de-tectability of a sensor [31]. These weighs can be obtained by fi-nite element-based vibration propagation analysis [44], and cal-ibrated by experimental modal analysis.The optimal functions include the maximum fault de-

tectability, and the minimum cost. Then an optimal model isbuilt based on quantitative signed directed graph (QSDG) of

Fig. 9. QSDG of fault vibration propagation in the gearbox.

Fig. 10. A prototype of ED/EP for the gearbox.

fault vibration propagation [44], which can be solved to deter-mine how many sensors, and where to embed them optimally.In this case, the optimal result corresponds to .

D. Integration and Application

Finally, a modified gearbox shown in Fig. 10 is designed andmanufactured according to the results, and a miniaturized vi-bration sensor module is designed [43], where the location ofsensor 1 corresponds to . Here, the slot is cut from the outerrace of the bearing. However, the outer race of the bearing is sta-tionary, so the slot and embedded minimized sensor module willnot cause unbalance for the gearbox. For rotating gears, we drillfour holes symmetrically to avoid unbalance in the gearbox. Itmust be noted that unbalance issues should be addressed in prac-tice, in particular on high-speed rotating structures.After assembling, the appearance of the gearbox is also sim-

ilar to traditional ones, except for an antenna. This configurationcan be used to test the feasibility and effectiveness of embeddeddiagnostics in the gearbox based on vibration signal analysis.An artificial fault is seeded in the inner raceway of a bearingon the input shaft, and its fault frequency is calculated to

be 60.2 Hz. Two meshing frequencies of high and low speedgears are 480 Hz, and 125.6 Hz respectively. Then vibrationsignals from three embedded sensors and an outside sensoron the housing are collected. These signals are analysed,and their power spectrums are shown as Fig. 11(a)–(d). InFig. 11(a) and (b), the fault frequency can be found. Also wecan see that the frequency interval (e.g. 30 Hz) in Fig. 11(b)corresponds to one half of the fault frequency. This observationshows the modulated relations between the half-harmonicsand other frequencies, which is indeed caused by the fault. Insummary, it is much easier to find frequencies related to thefault (about 60.2 Hz and its harmonics or sub-harmonics) inFig. 11(a)–(c) than in Fig. 11(d). Thus embedded sensing isbetter than traditional sensing for gearbox health monitoring.On the other hand, the fault frequency calculated from sensor


Fig. 11. Comparison of power spectrums from different sensors: (a) sensor 1;(b) sensor 2; (c) sensor 3; and (d) sensor on the housing.

1 is the most obvious among three embedded sensors becausesensor 1 is the closest to the faulty bearing, and the SNR ishigh. Furthermore, the location of sensor 1 corresponds toin the optimal solution, and it demonstrates that the optimalresult is feasible.Based on the case study, we can conclude that the ED/EP of

the gearbox can be utilized. It is more suitable for on-line healthmonitoring than traditional methods as it i) provides better SNR,ii) is more sensitive to faults, and iii) can be integrated moreeasily.

VI. CONCLUSIONS

Complex mechanical systems are increasingly used in mili-tary and industrial applications. Implementation of PHM is veryimportant to maintain the availability, safety, and economics.This implementation can be achieved through ED/EP, which isan emerging area for complex mechanical systems.This paper proposes a technical framework of ED/EP for

complex mechanical systems, and six key elements are summa-rized. Relationships among these key elements are outlined, andthey should be considered simultaneously at the design stage.This paper also looks into the roadmap of ED/EP for complexmechanical systems.While PHM work is abundant for mechanical systems, it is

not mature for ED/EP. Our goal is just to shed some light onthis research field. In the near future, the authors plan to focuson several fronts. i) We will work on wireless signal transmis-sion on rotating mechanical components. ii) The mechanicalstructure is often metallic, and it will shield RF signals fromembedded sensors, so a reliable wireless signals transmissionmechanism will be developed. iii) Power for embedded sensormodules is a challenge because the remaining life of a cellularbattery is limited. Energy harvesting (EH) is one potential so-lution which has been studied widely in recent years [45]–[47].iv) We plan to develop a prototype ED/EP system.

ACKNOWLEDGMENT

The authors would like to thank Prof. Q. Miao for his sugges-tions on this paper.

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Z. S. Chen was born in the Anhui Province, China, on Aug. 13, 1977. He re-ceived the B.E., and Ph.D. degrees in Mechatronic Engineering from the Na-tional University of Defense Technology, P. R. China in 1999, and 2004, re-spectively. From 2008, he worked as an associate professor in the Key Labora-tory of Science and Technology on ILS, College of Mechatronics Engineeringand Automation, National University of Defense Technology. His current re-search interests include mechanical signal processing and data fusion, condi-tion monitoring, embedded fault diagnosis and prognostics, and vibration en-ergy harvesting.

Y. M. Yang was born in the Hunan Province, China, on Apr. 20, 1966. Hereceived the B.E., and Ph.D. degrees in Mechatronic Engineering from the Na-tional University of Defense Technology, P. R. China in 1990, and 2008, respec-tively. From 1992 to 1998, he worked as a lecturer in Institute of MechatronicEngineering, National University of Defense Technology; from 1999 to 2003 asan associate professor, and then from 2004 as a professor in the Key Laboratoryof Science and Technology on ILS. FromMay, 1998 toMay, 1999, he worked asa visiting scholar in the Department of Mechanical Engineering at the Univer-sity of California, Berkeley, US. His current research interests include modelingand analysis of dynamic systems, condition monitoring and fault diagnosis, in-tegrated diagnostics, and vibration energy harvesting.

Zheng Hu was born in the Hubei Province, China, on Apr. 15, 1972. He re-ceived the B.E., and Ph.D. degrees in Mechatronics Engineering from the Na-tional University of Defense Technology, P. R. China in 1993, and 1998, respec-tively. From 2007, he worked as a professor in the Key Laboratory of Scienceand Technology on ILS, College of Mechatronics Engineering and Automa-tion, National University of Defense Technology. His current research interestsinclude Design for testability, embedded fault diagnosis and prognostics, me-chanical signal processing, and internet of things.