The present status and future growth of maintenance in US manufacturing: results from a pilot survey
The present status and future growth of maintenance in US manufacturing: results from a pilot survey
Xiaoning Jin 2
David Siegel 1
Brian A. Weiss 0
Ellen Gamel 1
Wei Wang 1
Jay Lee 1
Jun Ni 3
0 U.S. Department of Commerce, National Institute of Standards and Technology , 100 Bureau Drive, Stop 8230, Gaithersburg, MD 20899 , USA
1 Department of Mechanical & Materials Engineering, University of Cincinnati , 560 Baldwin Hall, 2600 Clifton Avenue, Cincinnati, OH 45221 , USA
2 Department of Mechanical Engineering, University of Michigan , 360 Huntington Ave, Boston, MA 02115 , USA
3 2300 Hayward St. , Ann Arbor, MI 48109 , USA
- A research study was conducted (1) to examine the practices employed by US manufacturers to achieve productivity goals and (2) to understand what level of intelligent maintenance technologies and strategies are being incorporated into these practices. This study found that the effectiveness and choice of maintenance strategy were strongly correlated to the size of the manufacturing enterprise; there were large differences in adoption of advanced maintenance practices and diagnostics and prognostics technologies between small and medium-sized enterprises (SMEs). Despite their greater adoption of maintenance practices and technologies, large manufacturing organizations have had only modest success with respect to diagnostics and prognostics and preventive maintenance projects. The varying degrees of success with respect to preventative maintenance programs highlight the opportunity for larger manufacturers to improve their maintenance practices and use of advanced prognostics and health management (PHM) technology. The future outlook for manufacturing PHM technology among the manufacturing organizations considered in this study was overwhelmingly positive; many manufacturing organizations have current and planned projects in this area. Given the current modest state of implementation and positive outlook for this technology, gaps, future trends, and roadmaps for manufacturing PHM and maintenance strategy are presented.
Maintenance strategy; Prognostics and health management; Preventive and predictive maintenance
Up-time improvement, waste reduction, and quality
optimization are three important metrics for manufacturing industries
to track and improve to enhance their capability and
competiveness. To realize these objectives, manufacturing industries
have developed several methods to evaluate manufacturing
processes and systems. For example, Nakajima proposed the
Overall Equipment Effectiveness (OEE) to evaluate the
utilization rate or efficiency of factory equipment [
]. Since its
inception, OEE has been widely adopted to evaluate factory
performance. Equipment precision and process health
condition are highly related to OEE, hence, there has been an
increasing interest in developing intelligent maintenance
systems to maintain or improve OEE in order to effectively access
equipment health condition and eventually predict and prevent
unwanted degradation and failures.
The increasing complexity of manufacturing equipment
and processes has forced maintenance to evolve perhaps more
than any other management discipline. Various maintenance
strategies have been developed, such as reactive maintenance
(RM), preventive maintenance (PM), and condition-based
maintenance (CBM). Improvements in network
communication, sensors, computing power, and machine automation have
made real-time prognostic devices, remote monitoring, and
self-maintenance emerging research topics on PHM
technologies for manufacturing. Despite increased interest in
prognostics and increased sophistication in maintenance,
manufacturers lack a standard process and methodology for using
PHM technologies on the shop floor.
The first step in developing a standard process and
methodology for PHM technology is to understand and define a
common set of performance metrics for productivity, maintenance,
and product quality that are being used by manufacturers. This
information allows one to quantitatively measure the
effectiveness of the various diagnostic, prognostic, and intelligent
maintenance activities under the same performance measures.
It is also important to understand the best practices in industry
for achieving maintenance and productivity goals. Surveying
different manufacturers can help identify these best practices
as well as less effective strategies.
1.1. Research objectives
The primary goal of this study is to investigate and define
the best practices used by United States (US) manufacturers to
improve their performance by incorporating intelligent
maintenance technologies and strategies into current practice.
The main objectives of this pilot survey are:
d survey the common metrics used by the manufacturing industry to
assess their productivity, maintenance and reliability, and product
d research the best practices that manufacturers are using to improve their
productivity, lower their maintenance costs, and improve their product
d assess the current state of the art in the manufacturing sector with
respect to diagnostic and prognostic activities, and review their past
successes and failures.
Overall, the results of the survey-based study will provide a
proper foundation for developing a set of standards and
methodology for developing and implementing intelligent
maintenance systems technology within manufacturing operations.
The outcomes of these defined research objectives will be used
to help direct the future research direction and goals for
developing a unified methodology for intelligent maintenance
systems for manufacturing systems.
The results from this study could determine several
important aspects, including (1) whether there is a statistical
difference between the number of successful implementations
of diagnostic activities for large manufacturers when
compared with small and medium-sized manufacturers and (2)
developing an understanding of the common challenges for
manufacturers for implementing prognostic and diagnostic
technology. The reporting of these key findings and statistical
results would be imperative for understanding the current
status and needs of the manufacturing industry, and would be
later used to develop appropriate standards for prognostic
and diagnostic activities that address the identified needs in this
1.2. Literature review
Table 1 presents the historical evolution and future
development of maintenance practice. The following subsections
provide greater detail for each maintenance strategy in Table 1.
Reactive maintenance (RM) is a corrective action applied
on observable failures or unanticipated threats of failures.
RM has relatively low initial cost while it may increase the
costs of unscheduled equipment downtime and production
losses. RM is not recommended if a failure can endanger
personnel, interrupt production, or cause collateral damage.
Preventive maintenance (PM) involves the repair,
replacement, and maintenance of equipment to avoid unexpected
failure during operation. The objective of any PM program is the
minimization of the total cost of inspection, repair, and
equipment downtime (measured in terms of lost production capacity
or reduced product quality). Even a successful PM strategy that
improves equipment availability has two drawbacks: (1)
timebased or operation count-based PM programs lead to possible
under-maintained or over-maintained equipment, especially in
instances when the PM interval is predetermined without
considering various operation regime shifts; and (2) replacing the
component before it severely degrades or fails does not allow
for insightful information to be learned about the equipment’s
These drawbacks do not make PM the most cost effective
program option. Eventually, preventive maintenance becomes a
major expense for many industrial companies. Therefore, more
efficient maintenance approaches, such as predictive
maintenance (PdM) are being implemented.
Predictive Maintenance (PdM) is a right-on-time
maintenance strategy. It is based on the failure limit policy in which
maintenance is performed only when the failure rate or other
reliability indices of a unit reaches a predetermined level.
PdM can be classified into reliability-centered maintenance
(RCM) and condition-based maintenance (CBM). However,
this maintenance strategy has been implemented as CBM in
most production systems where certain performance indices
are periodically [
] or continuously monitored . CBM
is a technique or a process for monitoring the operating
characteristics of machines (or components). Changes and trends in
the monitored characteristics can be used to predict the need
for maintenance before serious deterioration or breakdown
occurs. Thus, CBM attempts to avoid unnecessary
maintenance tasks by taking maintenance actions only when there
is evidence of abnormal behavior in a piece of equipment or
process. By reducing the number of unnecessary scheduled
preventive maintenance operations, a properly established and
effectively implemented CBM program can significantly reduce
maintenance costs [
]. For example, based on a high-level
analysis of the automotive industry, Barajas and Srinivasa 
stated that the best return on investment (ROI) is achieved
through predictive maintenance as opposed to reactive or
Prognostics and Health Management (PHM) focuses on
understanding the failure modes, detecting precursors to
failure, tracking degradation mechanisms, and predicting the
remaining useful life of components and systems. While
effective to a certain degree, neither preventive nor predictive
maintenance is geared to detect the most common and subtle failure
causes, such as contamination or leakage. PHM can be used to
determine the root causes of failures, predict degradation
trends, and take corrective actions to eliminate the sources of
failure before problems occur.
In summary, the decisions to implement a proper
maintenance program must be based on the probability and
magnitude of the failure along with the associated costs and
consequences. Designing an effective and efficient
maintenance strategy requires engineering efforts that optimize the
relationship between equipment ownership and operating
profits by balancing cost of maintenance with cost of equipment
degradation and failures and associated production losses.
PdM and PHM usually require a higher maintenance cost
due to higher requirement for technology readiness, but can
substantially save unnecessary failures and extend the life of
equipment than can simple RM and PM.
1.2.1. Manufacturing prognostics and health management
Manufacturing-related prognostic and health management
(PHM) research work is divided into machine-level and
system-level studies, which highlights the greater emphasis found
in the literature on machine-level PHM. Much of the
machinelevel research focuses on machine tools, including the machine
tool spindle [
], cutting tool wear or breakage [
], and the
machine tool feed-axis system [
]. For machine tool
applications, the PHM algorithms used by researchers included
both data-driven and first-principle methods, however,
datadriven classification algorithms [
10, 12, 13, 16
] were more
popular than first-principle methods. Algorithms, such as
support-vector machines (SVM), self-organization maps (SOM)
and variations on neural networks, and fuzzy-based diagnosis
systems were some of the classification methods used in these
machine tool studies. Model-based methods for machine tools
were also considered by researchers, including the work by
Cao et al. . Cao et al. [
] used a first-principle model of
the spindle to determine the optimal sensor location for spindle
bearing health monitoring. Without a physical model, one
might incorrectly place an accelerometer that has a
lessthan-desirable transfer path with respect to the fault location,
which could reduce the sensitivity of the monitoring system
to a bearing-related failure. Some challenges in implementing
machine-level PHM in production factories are still
unresolved, including how to automatically update the health
models due to maintenance activities and obtaining sufficient data
in a factory to validate machine-level PHM models.
For manufacturing system-level PHM, the work by
Muthiah et al. [
] introduced a new metric called overall
throughput effectiveness, which can provide a beneficial way
to benchmark the factory’s current performance with a baseline
number. Besides offering a way to monitor and trend the
factory performance, this proposed metric offers promise for
detecting factory bottlenecks, which would help diagnose a
resultant drop in factory performance. Even for conventional
metrics, such as Overall Equipment Effectiveness (OEE), it
was noted that successful use of OEE depends on the ease
of collecting the data and providing human-consumable
]. This sample of PHM manufacturing studies
highlights the gaps in system-level diagnostics and prognostics
2.1. Survey questionnaire development
The survey questionnaire was designed to cover a broad
range of manufacturing industry sectors. The questions were
designed based on different perspectives of maintenance
practices and divided into five parts:
1. performance metrics for productivity, intelligent maintenance, and
2. maintenance strategy and effectiveness;
3. key factors that affect maintenance performance;
4. common problems, failure modes, and bottlenecks for manufacturing
process and equipment;
5. future TRENDS for PHM technology for smart manufacturing from an
The questionnaire contained questions pertaining to these
five parts. All questionnaire items were operationalized using
several categorical, ordinal questions and interval questions,
a well-accepted practice in survey. Many of the respondents
used subjective measures based on their daily observation
and estimation. Although the use of objective measures would
have been more desirable, it has been difficult to acquire exact
data for a variety of reasons (e.g., limited data collection
capability, confidentiality, accounting conventions, etc.)
2.2. Sample and data collection
The data were solicited via questionnaires, phone
interviews, and on-site facility visits with a variety of
manufacturing enterprises ranging in size. A total of 15 manufacturing
enterprises and eight technology/consulting companies
provided responses to the questions through surveys and
interviews with manufacturing managers, maintenance managers,
and other senior professionals within the manufacturing
facility. The profile of the respondents is shown in Table 2.
Manufacturing enterprises provided the most direct responses to the
questions based on their own maintenance strategy, operations,
and practices in PHM development and implementation, while
technology/consulting companies provided more
comprehensive information such as common PHM solutions to various
types of industrial sectors.
In particular, the manufacturing enterprises were classified
into two general groups based on their sizes: (1) large-sized
enterprises, and (2) small and medium-sized enterprises
(SMEs). Although there is no universally accepted definition
of SME, this study uses the thresholds established by the US
Department of Commerce as guidelines, i.e., SMEs are
manufacturing enterprises with less than 500 employees and
largesized enterprises with over 500 employees. Moreover, the
enterprises represent various sectors within manufacturing,
including: automotive, aerospace, transportation, machinery
and equipment, consumer products, and electronics.
3.1. Observations and themes from questionnaire
The questionnaire responses that were collected during the
site visits, plant tours, and phone discussions were organized
into categorical bins for bar charts. Statistical hypothesis
testing was conducted to help summarize the overall sentiment
and themes found across the various manufacturing enterprises
and technology consulting companies who participated in the
According to the survey data, we classify the commonly
used measures of maintenance performance into five categories
based on their focus. They are:
d measures of equipment performance (e.g., availability, reliability, mean
time to failure);
d measures of product quality performance (e.g., defect rate, yield);
d measures of maintenance productivity performance (e.g., manpower
d measures of maintenance cost performance (e.g., maintenance labor
and material cost);
d measures of safety and environment (e.g., safety, health and
Figure 1 shows the percentage of the maintenance
objectives pursued by the manufacturing facilities according to the
respondents’ selection of important maintenance objectives
within their plant.
Several key findings drawn upon this analysis are discussed
as follows. First, it was noted that safety, availability, and
reliability are the most highly rated maintenance objectives.
Productivity and quality are also important maintenance
objectives according to 70% of the respondents. It is also noted
that with different focuses of manufacturing performance
goals, maintenance managers have different objectives of their
maintenance function. Second, equipment performance-related
indicators are very commonly used. Equipment performance
indicators are measured on a shorter time interval (on a daily
3. Results analysis and discussion
or weekly basis) than cost performance measures (on a
monthly or quarterly basis).
Figure 2 shows the performance metrics used by
responding manufacturing organizations, including productivity,
maintenance, and product quality. In general, a larger sample size of
manufacturing enterprises would be needed to draw more
statistically significant conclusions, but it does appear that the
majority of the manufacturers surveyed use a combination of
metrics (e.g., throughput metrics, part quality metrics,
maintenance metrics), shown as the first bar in Figure 2. The idea that
a manufacturing enterprise does not rely solely on a single
category of metrics (e.g., only part quality or only maintenance) is
well aligned with the research about companies adopting the
OEE metric to measure their factory performance over time.
Some important insights were also gained on whether
CBM strategies for certain types of machines or processes
had been considered by manufacturing organizations surveyed
in this study. The responses for this particular question are
provided in Figure 3; there appears to be a vast majority of
organizations that have started to consider condition-based
A Chi-square statistical hypothesis test is performed to see
if there is any statistical evidence that the responses to this
question (consideration of CBM) are random (null hypothesis)
or if there is evidence against that hypothesis. The Chi-square
statistical test procedure for categorical variables consists of
comparing the expected bin frequencies to the observed bin
frequencies. Based on the hypothesis that the responses are
random, one would assume an expected frequency count that
was even for each bin group (e.g., if there are 20 samples
and four bins, one would expect five counts in each bin).
The Chi-square test statistic (v2) is given by the expression
provided in equation (1) where n is the number of bin groups.
The degree of freedom for this statistical test is based on the
number of bin categories as indicated in equation (2). The
results from this statistical hypothesis test are shown in Table 3
in which the test-statistic and p-value indicate that there is
evidence that the responses are not random. Although the sample
size is relatively small, it does provide some general
observations that manufacturing organizations are starting to move
towards condition-based maintenance strategies.
df ¼ n
Although it was observed that companies are starting to
consider condition-based maintenance, it was interesting to
see if they had current or past diagnostic or prognostic projects.
The responses in Figure 4 indicate that a majority of the
manufacturing companies had active projects in manufacturing
diagnostics and prognostics, while a few had no current
projects but had some past projects. It was noted during a
conversation with one of the maintenance managers for a
construction machine manufacturer that not all of their past
diagnostic projects were successful and some project results were
disappointing in being overpromised by the diagnostic and
A Chi-square test was also performed on the bar chart
results in Figure 4 to see if there was any evidence that the
responses were not random and that the manufacturers had a
significant sentiment to a particular response bin. For this
question, the Chi-square test statistic was at 6.09 and the p-value was
also above the alpha level, indicating that there was not enough
evidence to reject the null hypothesis that the distribution of the
responses was uniform. Perhaps additional manufacturer
companies would need to be included in this study to determine
if there was any underlying pattern or trend with respect to
current or past diagnostic and prognostic related projects (Table 4).
Most manufacturers are using a combination of metrics
that consider part quality, throughput, and maintenance
effectiveness. In addition, it was found that the effectiveness
of the preventative maintenance programs varied across the
manufacturing organizations surveyed. It is postulated that
the manufacturing facility size and diversity and age of their
assets contributed to the variance in these responses. Also,
the vast majority of manufacturing organizations surveyed in
this study are starting to consider condition-based maintenance
approaches and technology. Many of the manufacturers
surveyed had active diagnostic and prognostic projects, although
a few mentioned past projects that achieved varying degrees
of success. Technology providers and manufacturers had a
positive and optimistic viewpoint when considering the future
outlook for manufacturing PHM.
3.2. Maintenance factors analysis
Building on the questionnaire results presented in
Section 3.1, the responses from the manufacturers are further
quantified to identify differences between different types of
Level 1 (33.3%) reactive
Maintenance has significant room
for improvement, or preventive
maintenance program is lacking/
Rely heavily on reactive
maintenance (RM), no equipment
health information involved.
Less than 50% work planned accomplished. High overtime (>30%).
Have no CBM or PHM. Low involvement of management. Reactive maintenance is very common.
No training on how to use PM or
other maintenance strategies.
Lack of system to collect
maintenance knowledge. No team
that is responsible for developing
and implementing prognostic and
Overall Equipment Effectiveness
(OEE) is less than 50%.
‘‘Fire Fighting’’ approach.
manufacturing organizations. Each factor is divided into three
levels, which are advanced, intermediate, and reactive. The
eight key factors and the descriptions of three levels of each
factor are detailed in Table 5. The three levels presented in
Table 5 are explicitly defined for this study to increase the
clarity and structure of the responses for the participants.
In order to study how enterprise size may influence these
key factors of maintenance, the responses to the interval
questions (based on Table 5) are averaged and plotted in radar
charts for large-sized enterprises and SMEs, respectively.
Figure 5 presents the average levels of eight key factors for
large firms and SMEs. The radar charts display the eight key
factors with averaged responses, where the further the data
points are from the center, the better performance the
Some differences can be observed clearly between the two
radar charts in Figure 5: company size influences the key
factors and the levels of large firms are generally more
advanced than SMEs, particularly with respect to the
maintenance effectiveness level, maintenance strategy level,
profitability level, continuous improvement level, human factor
level, and organizational readiness level. Meanwhile, the
average levels of some other key factors, such as task planning and
scheduling level and TPM level, are similar between large
firms and SMEs. The radar chart can only present a general
comparison. Whether the difference between large firms and
SMEs is statistically significant should be further determined
by using correlation analysis and hypothesis testing, as seen
in the following sections.
3.3. Contrasting SMEs and large-sized manufacturers
The correlation analysis indicates that a relationship exists
between the size of manufacturing enterprise and the eight key
factors; the Student’s t-test is adopted to do the hypothesis
testing to see whether the differences between SMEs and
largesized manufacturers are statistically significant.
Hypothesis testing using Student’s t-test
Due to the small sample number, the Student’s t test is used to
check whether there are significant differences in each factor
between large manufacturers and SMEs. All eight factors in
Table 5 are tested between SMEs and large-sized manufacturers.
An example of maintenance strategy level for SMEs versus large
firms is presented below to explain how the hypothesis test works.
The null hypothesis on maintenance strategy level H0 is
that the mean maintenance strategy level of SMEs equals the
mean maintenance strategy level of large-sized manufacturers.
The results of a student’s t-test are shown below.
First, Levene’s test is used to check whether the variances
of two groups are equal because Levene’s test is an inferential
statistic used to assess the equality of variances for a variable
calculated for two or more groups. The significance of F-value
is 0.023, which is less than 0.05, meaning that the variances in
the two groups are not equal, i.e., equal variance is not
assumed. According to Table 6, two-tailed t(0.05, 9) is less
than the absolute t-value, i.e., |t| > t(0.05, 9). Therefore, H0
is rejected, indicating that the mean maintenance strategy level
of large-sized manufacturers is significantly larger than the
mean maintenance strategy level of SMEs.
Two key findings from the statistical hypothesis tests are:
(1) the mean levels of maintenance effectiveness, maintenance
strategy, profitability, continuous improvement, HR, and
organizational readiness of large-sized manufacturers are
significantly larger than those of SMEs; and (2) there is no
significant difference between the mean levels of scheduling
and TPM of large manufacturers and SMEs.
Overall, the results suggest that large manufacturers, in
contrast to SMEs, have the ability to focus on two distinct
strategies: (1) continuous improvement on intelligent maintenance
technology and quality on one hand, and (2) a combination of
low-cost maintenance and innovation on the other hand. This
latter strategy is particularly interesting since it denotes
simultaneous emphasis on both cost-effective PHM technology
innovation and strategy innovation.
3.4. Change efforts and barriers
The results also reveal significant differences between
SMEs and large companies in their change efforts in improving
maintenance practice and PHM technology. Whereas both
SMEs and large enterprises are affected by hyper-competition
and accelerated pace of change, SMEs appear less able and/or
less willing to initiate change in their maintenance functions,
mainly because of size-related disadvantages. They are faced
with more barriers for change in terms of organizational
structure and readiness for innovation and the associated limited
finance and human resources.
To illustrate the barriers clearly, the respondents’ concerns
about future efforts on PHM technologies are presented in
Figure 6. Figure 6 shows the factors that might be the barriers
for companies considering to change their current maintenance
practice and/or invest in more advanced CBM/PHM
technologies. Cost is the major concern for both large and small
companies. Technical support from a R&D team for CBM and
PHM technology implementation is also an important barrier
for both but more critical for SMEs.
4. Gaps, future trends, and research directions
Some underlying gaps and trends were observed in this
study, and these observations will provide some clarity with
respect to the research directions for manufacturing PHM. It
should be noted that the sample size in this study was small
and a larger pool of manufacturers should be considered as
an extension to this study to better validate these initial
observations and trends that were observed. One of the general
observations from this study was that the maintenance strategy
level is relatively low, and most manufacturing enterprises
willing to improve their maintenance strategies are facing some
barriers, such as cost, workforce, technology readiness, system
design changes, etc. In addition, large enterprises are making
more effort to improve their maintenance strategy because of
their size-related advantages such as R&D support and
leadership involvement, skilled workforce, and other resources.
Besides these more economical barriers for adopting a
more condition-based maintenance strategy, the overall state
of the art for manufacturing PHM has many current gaps.
In particular, the literature survey highlighted that while there
is substantial work on component and machine level
prognostics and diagnostic research, there is very little prognostics or
diagnostic research work that considers multiple machines or
a production system. Although it was noted by some
technology providers that a system level health monitoring system
would be more difficult to achieve, it appears that at least some
fundamental research work should be started for the system
level diagnostics and prognostics.
In addition, even current machine-level prognostic and
diagnostic implementations have current gaps which are
limiting its success when implemented by manufacturers. In
particular, it seems that more reliable threshold methods and more
adaptive machine-level health monitoring models are needed.
These would help address some of the manufacturers’
comments on current implementations, which had an unsatisfactory
number of false alarms and also had difficulty in collecting
baseline data that contains all the possible machine cutting
settings and operating modes. It was also noted by the technology
providers that the lack of failure data makes it more
challenging to develop robust prognostic and diagnostic methods for a
variety of reasons. Without reference data sets that include
failure data, validation becomes very difficult.
Beyond the technology gaps, there is a lack of buy-in
accompanied by low industry-PHM awareness, experience,
and training needed to apply principles and tools and what type
of value it can bring. To inspire more enterprises, particularly
SMEs, more manufacturing PHM case studies should also be
presented, whether they document successful or failed PHM
attempts. From successful case studies, the enterprises can
know how to increase the return on investment using CBM
and PHM because the cost is the most significant barrier.
From the failed case studies, the root-cause can be explored.
More case studies can help enterprises know exactly how
CBM and PHM work, and then, inspire more enterprises to
consider CBM and PHM. The case studies should not only
focus on the research, but also focus on the cost, i.e., case
studies should show customers how CBM and PHM decrease the
cost and increase the return on investment. The gaps and
barriers for implementing advanced PHM technologies and
maintenance strategies in this study agree with the gaps
identified in the NIST PHM workshop report [
This survey-based pilot study will be used by the
stakeholder community to guide future directions for the
development of new technologies and infrastructure to support PHM
system implementation in smart manufacturing environments.
Table 7 shows the research efforts that are expected to address
the key elements of future PHM roadmaps at multiple levels.
5. Conclusions and future work
This study was aimed at investigating manufacturing
industry best practices that the manufacturing industry is
currently using to achieve their performance goals by
incorporating intelligent maintenance technology and strategies. With
that notion in mind, data was collected by phone interviews
and on-site facility visits from various manufacturing
enterprises, including a total of 15 manufacturing enterprises and
eight technology/consulting companies. One of the interesting
findings during this study was that the maintenance
effectiveness, maintenance strategy, and human resources allocation
for maintenance were significantly correlated with the size of
the manufacturing enterprise. There was a clear difference in
maintenance technology and strategy when comparing large
and SME manufacturing enterprises. Even for the larger
manufacturing enterprises, it was noted that the effectiveness of
their preventative maintenance programs varied between the
different organizations and many organizations had mixed
success with respect to their past diagnostic and prognostic
projects. Despite this mixed level of success, many of the
manufacturing organizations surveyed had active diagnostic and
prognostic projects and had an overwhelming positive and
optimistic viewpoint when considering the future outlook for
Priority of research effort
High High Medium
High Medium Medium
Medium Medium High
Medium High High
The results from this study illustrate many future research
directions to address the gaps identified in this study. The
literature review highlighted a sparse set of technical work on
system-level PHM for factory applications, in comparison to the
machine-level and component-level PHM work for robotics,
machine tools, and other manufacturing equipment; thus the
need to develop technical approaches for system-level PHM
for factory applications is one potential future research
direction. In addition, some manufacturers were disappointed
in the threshold setting and overall robustness in the PHM
machine-level models; this reiterates that there is a still a need
to improve the current state of the art with respect to PHM for
manufacturing components and machines. Lastly, there is a
significant gap between SME and large manufacturing
organizations, in which the SME would benefit from at least learning
from the large manufacturers and their early trials and success
with PHM and maintenance technology. With this notion, it
would be beneficial to make a concerted effort to disseminate
the PHM manufacturing case studies, with the aim that SME’s
would eventually consider adopting these maintenance
technologies as they see fit.
Besides the gaps and issues being highlighted in this paper,
many other issues and challenges that prevent manufacturers
from adopting advanced PHM technologies will be further
explored and discussed in the future work, such as the need
for using digital technologies for data collection and handling
and interpreting ‘‘industrial big data’’, the need to develop
protocols and tools to communicate data, information, and metrics
across the component, machine and system levels for
diagnostics and prognostics in manufacturing, and the need to enhance
operations and maintenance intelligence by predictive and
preventive control and management.
This survey focused on US-based manufacturing industry.
The state of the art and the trend of PHM technologies in
the US manufacturing can be related to broader impact on
technology development in other countries in Europe and Asia.
A large proportion of US manufacturers’ R&D takes place in
high technology sectors such as electronics and aircraft
manufacturing, whereas in most other counties a far greater portion
of manufactures’ R&D outlays occur in medium-technology
sections such as motor vehicle and machinery manufacturing.
Therefore, R&D spending in PHM technology in the US
manufacturing appears to be more important and value added.
Being one of the leading countries in manufacturing, the US
will be taking a leading role in the future PHM technology
and research development and making broader global impact.
Likewise, several of the US manufacturers surveyed have a
presence in other countries across the globe. Best practices
and lessons learned within a facility inside the US are likely
to be disseminated to their foreign counterparts to improve
overall productivity and efficiency. In addition to individual
companies having footprints across multiple countries, many
supply chains span the globe. That is to say a US company that
resides within the same supply chain (whether it be upstream
or downstream) as a foreign entity is more likely to share their
own CBM and PHM best practices and lessons learned if it’s
mutually-beneficial. Some of the research needs and directions
mentioned above (e.g., smart sensors, data collection and
communication; performance metrics, and assessment; and
workforce skill and training) would also bring tremendous value
to the international community with their input and
involvement. Supply chains are so interconnected on a global scale
that a fault or failure at a specific node in the supply chain
can cascade beyond the home country of the fault. Given this
phenomena, one can theorize that enhancing one’s CBM and
PHM practices and disseminating these findings to its
collaborators can have a positive cascading effect.
Acknowledgements. We acknowledge the support of the National
Institute of Standards and Technology (NIST), US Department of
Commerce in providing the grant support (#70NANB14H234) and
intellectual input into this pilot study.
Certain commercial systems are identified in this paper. Such
identification does not imply recommendation or endorsement by
NIST; nor does it imply that the products identified are necessarily
the best available for the purpose. Further, any opinions, findings,
conclusions, or recommendations expressed in this material are
those of the authors and do not necessarily reflect the views of NIST
or any other supporting US government or corporate organizations.
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