A MIMOSA White Paper
on
PROGNOSIS OF REMAINING MACHINE LIFE BASED ON CONDITION
by
Carl Talbott, D.Sc.
ctalbott@ix.netcom.com

January 24, 1998

Background:   During the past decade, many industrial manufacturing organizations have implemented condition monitoring programs on production machinery.  The objective is to track various condition and performance measurements related to a specific machine (or component of a machine, e.g., electric motor) over time in order to deduce pending machine failures. 

By taking proactive action to avoid unscheduled production downtime, these organizations expect to reduce the costs of maintenance, direct labor, and waste related to the conversion of raw materials into finished goods.  Such action presupposes a capability to diagnose pending failures within a sufficient lead time (depending on the contemplated action).  However, development of condition monitoring technology has, in the past, focused on diagnostic aspects rather than on predicting residual machine life.  

Unfortunately, this "prediction" issue has been misconstrued within the maintenance and reliability community as a time-to-alarm question with end-users expected to set their own alarm levels based on experience and engineering judgment.  As a consequence, end-users tend to schedule machinery maintenance based on (subjective) alarm criteria rather than on objective estimates of remaining life.  Such "on-condition" scheduled maintenance wastes an (unknown) amount of remaining life and does not afford the end-user a means of economically evaluating or managing risk.  An accurate prognostic capability, on the other hand, provides end-users with value-added information for asset management decisions.

In an earlier paper [1], we summarized and critiqued some of the available literature on prognosis of residual machine life.  In this earlier review, we defined a notion of prognosis and concluded that existing methodologies do not satisfy end-users' needs.  Here, prognosis is a point estimate of the remaining life of a machine (or component) based on one or more condition or performance signals observed at some point during the machine's life.  In order to provide decision-making utility to end-users, this point estimate of residual machine life also should have a stated confidence bounds, e.g., four months of remaining life plus or minus one week at 90% confidence.  

In addition, we suggested end-users should have access, upon demand, to the accumulated knowledge used to create individual machine prognostic products so as to be able to judge the validity of the prognostic process.  This "audit-trail" can protect the prognostic service provider from legal liability in that relevant data as well as the analysis methodology are "open" to examination and evaluation by interested clients.  

In this paper, we describe a data harvesting methodology to assemble a number of condition-based maintenance histories of like machines known to have all failed due to the same failure mode.  The resultant prognostic “knowledge-base” serves to describe remaining life as a function of one or more machine condition signals.  In another soon-to-be-published paper [2], we illustrate how these knowledge-bases can be used to generate prognoses.  However, our purpose here is to outline several machine history data collection issues that call for standardized practices.  

The question, then, is for MIMOSA to decide if consensus practices for prognosis are an appropriate MIMOSA development issue and, if so, to coordinate appropriate resources for their resolution. 

Machine Condition History:  We can think of a machine's condition measurements (of say "n" different condition or performance variables) at some age, x, during the machine's life as a condition vector, c = {c1, c2, ... cn}.  A series of these measurements made during the machine's life (say "m" condition vectors) can be represented by a condition matrix, C, of order (m x n).  The value cij within the ith row of the jth column of C, then, represents the jth condition signal of the ith measurement made at age xi during a specific machine's life.  Note that the "m" condition vectors need not represent measurements taken at equally spaced time durations; however, the "n" different condition signals are considered to be taken simultaneously at age xi in the machine's life.  

For example, the condition matrix, C, could represent the entire condition history of an intelligent machine with a variety of condition monitoring sensors on-board (vibration, temperature, power, etc.).  Condition vectors c can contain different type signals (vibration, temperature, etc.), signals from same-type sensors at multiple locations (as in two accelerometers for the same bearing located 90o apart), and artifacts of a single signal (i.e. vibration  at some frequency band).  For instance, a machine's vibration "waterfall" plot based on 400 lines of resolution and 100 measurements taken over time can be represented by a (100 x 400) condition matrix C.

Now, suppose the machine that we are monitoring "fails" in-service at some age (or calendar time), x0, such that x0 > xi for all i.  And suppose, for reasons that will become clear later, we identify the failure mode via machine autopsy by a qualified examiner.  The residual life, ri, at machine age xi, can be calculated as ri = (x0-xi) and can be associated with a residual life vector, r = {r1, r2, ..., rm}T of order (m x 1) where r1>r2> ... >rm.  The definition of "failure", here, is important since we want to aggregate a number of machine condition histories into a knowledge-base as discussed below. 

Knowledge-Base Construction:  We can represent a failed machine's entire life history with a partitioned matrix, H = [C,r] of order m x (n+1) having r as its final column (see [3] for notation).  Now, consider the life histories of a number of like machines (say "p" of them) that are known to have all lived in similar operating environments and failed according to the same failure mode.  

The array of partitioned matrices, [H] = {H1, H2, ..., Hp} is a three dimensional means of aggregating and storing all the relevant condition and residual life history data necessary for prognosis.  The ordering of {H1, H2, ..., Hp} within [H] is unimportant; as additional machine histories become available, they can be appended to [H] in any order.  Also, in practice, the order of Hi may not be the same as Hj in that one machine's history may consist of more or less measurements or condition signals than another same-type machine's history.  We can enlarge the order of [H] to accommodate the largest order Hi and add null matrices as appropriate to smaller order Hj so as to "fill-in" [H] without adverse effect. 

We call [H] a prognostic knowledge-base unique to a single failure mode in same-type machines that have all lived in the same-type operating environment.  Clearly, there exists a large number of different [H] for various failure modes, different type machines, and different operating environments.  The challenge within the maintenance and reliability community is to populate these prognostic knowledge-bases with germane field data in such a manner as to create the capability to generate prognostic products.  

To this end, there are some prognosis information issues, as discussed below, that beg for standardized practice and common data exchange protocols in order to facilitate the consolidation of machine histories from a large number of diverse machinery end-users. 

Issue: Definition of Machine Failure.  One of the requisite determinations for tracking residual machine life is a common (consensus) definition of machine failure.  Just as definition of human death is needed within the medical profession, we need a common understanding of when a machine has "failed" and has no more remaining life.  Some say "a machine is "failed" when it is no longer useful in its operational context" [4] and leave the definition of operational context to individual end-users (e.g., too noisy, not safe within acceptable confidence limits, won't produce within acceptable tolerances, etc.).

An alternative to many subjective definitions of machine failure is to establish a single performance-related description of failure for a given type machine as a consensus definition.  For example, we might all agree that an electric motor is "failed" when it no longer converts good quality electricity into sufficient torque to move its requisite load or that a pump failure occurs when it no longer converts good quality torque into flow of its pumped medium in the context of its static load.  

Recommendation:  MIMOSA develop consensus definitions of performance- specific failure for each machine type contained in MIMOSA's set of lookup table entries.  

Issue: Machine Autopsies.    In order to aggregate machine histories from many different end-users according to failure mode, we need a common protocol for identifying and reporting machine failure modes.  Just as governmental public health services collect death certificate statistics on the human population, we need similar data collection protocols for the machinery population that facilitate the cultivation of accurate machine histories from condition-based maintenance programs across diverse set of machine-types and end-users communities. 

In addition to a common definition of failure for same-type machines, we need to ascertain root cause failure modes (rather than failure effects) via machine autopsy conducted by competent technicians.  One strategy is to create standard Failure Mode & Effects Analyses (FMEA) for common same-type machines, e.g., one horsepower three-phase ac induction electric motors, industrial dc motors, brushless servo motors, etc.  A structured autopsy, then, seeks to identify not only the failure effect (damaged component) but also its root cause.  For example, electric motor bearings are sometimes identified as the "cause" of failure when, in fact, stray electric currents flowing in the motor's rotating shaft result in electrical arcing and subsequent surface pitting in the bearing race.  The root cause failure mode, in this situation, is stray electrical current in the motor's shaft rather than a defective bearing.  

Recommendation:  MIMOSA document and codify common machinery FMEAs.  MIMOSA machine autopsy and "death certificate" conventions could be developed over time as resources permit and disseminated worldwide at no fee via the Internet.  Moreover, these conventions could be "translated" into a variety of languages so as to facilitate international collection of machine histories.

To ensure good quality machine autopsies, we need qualified machine examiners knowledgeable of  failure modes and forensic evidence for the machines they examine.  The Vibration Institute and other condition-based maintenance training providers that provide technician certification programs could include machine autopsy training as part of their curricula.  Each machine history Hi, then could be accompanied with the identity of the examiner who performed the autopsy so as to provide accountability and ensure (as much as possible) reporting integrity.

Recommendation:  MIMOSA incorporate data fields as necessary to identify a machine autopsy examiner into its prognostic data protocols.

Issue: Ancillary Machine History Data.  Along with machine examiner identity, there are other identifying details which should accompany each machine history matrix Hi.  At a minimum these ancillary data should include:

-  Machine brandname, model number, date of manufacture, serial number, and other identifiers than can be used to segregate machine histories into relational groups.

-  Machine root-cause failure mode effects plus any secondary failure modes that may be evidenced in the machine's condition history.

-  Operating environment details in terms of average load, ambient temperature, power quality, and other environmental considerations such as indoor/outdoor service that may contribute to accelerated machine failure.

-  Repair cycle number and repair service identification (in-house, third-party contract, OEM) for machines that have experiences more than one lifetime as the result of repair.  These data are essential to evaluating assumptions of repair to "good-as-new" condition.  With these data, we can study subpopulations of same-type machines to quantify "goodness" of repairs.

Recommendation:  MIMOSA incorporate the above ancillary machine history details into its prognostic data protocols.

Issue: Residual Life Testing.  Another important implementation issue concerns the availability of in-service machine failures.  Most industrial organizations involved with condition-based maintenance want to prevent unscheduled machine failure and, therefore, discard or overhaul machines before they fail in-service.  

Rather than lose near-end-of-life condition data, we propose development of a network of unbiased laboratory providers that can perform residual life tests on machines removed from service for cause.  These providers could (for a fee) replicate operational and environmental conditions, instrument machines as appropriate, and run them until failure.  Such laboratory capabilities already exist for some types of machines.  For example, the Oak Ridge National Laboratory has an Electric Machinery Systems Testing Facility for testing electric motors and a Pump Flow Test Loop for evaluating pumps as part of their Oak Ridge Centers for Manufacturing Technology [see reference 5 and web page www.ornl.gov/orcmt]. Penn State has a Mechanical Diagnostics Test Bed for 12-20 horsepower, single and double reduction gearboxes [6].  And then, Oregon State has a Motor and Drives Resource Facility for testing adjustable speed drives and electric motors [see web page www.ece.orst.edu/tech].

Recommendation:  MIMOSA incorporate the identity of the residual life testing facility into its prognostic data protocols for machines removed from field service for cause and later run-to-failure by an independent agency.

Machine end-users (who own these sick machines removed for cause) could loan or donate their machines to these residual life testing providers along with their machines' condition histories Ci and ancillary data (since the machines have not yet failed, residual life data does not yet exist).  These data, of course, are intellectual property whose successive ownership are a matter of contractual agreements.  Individual end-users may want to retain ownership (as well as access) rights to these machine history data as they are converted into diagnostic and prognostic information.  The pooling of machine histories from a large number of end-users adds value to this data conversion process; however, ownership of pooled data intellectual property (and the right to market its derivative products) is a legal issue.

Summary:  The science of residual machine life prognosis, as discussed above, will require substantial cooperation and collaboration between machinery OEMs, end-users, and service providers.  MIMOSA is in a unique position to coordinate and sustain this information infrastructure development effort so as to ensure an "open" prognostic information systems architecture and to facilitate the population of prognostic knowledge-bases for the benefit of machinery end-users world-wide.

References:

1.  Talbott, C.; "State of the Art in Prognosis of Residual Machine Life"; Proceedings, Maintenance And Reliability Conference (MARCON97); sponsored by the Maintenance and Reliability Center, College of Engineering, The University of Tennessee, Knoxville; May 20-22, 1997; pp. 38.01-38.11.

2.  Talbott, C.; "Prognosis of Remaining Machine Life Based on Condition"; Proceedings, Society for Machinery Failure Prevention Technology Meeting;

3.  Marlow, W.H.; Mathematics for Operations Research; John Wiley & Sons; New York; 1978; pp. 22-26.

4.  Nickerson, G.W. and D.L. Hall; "Research Imperatives for Condition-Based Maintenance"; Proceedings of the 8th International Congress on Condition Monitoring and Diagnostic Engineering Management (COMDEM95); Kingston, Ontario; 26-28 June 1995; pp. 203-210.

5.  Kueck, J.D., D.A. Casada, & P.J. Otaduy; "A Comparison of Two Energy Efficient Motors"; P/PM Technology; Vol 9, Issue 2 (April 1996); pp. 46-51.

6.  Kozlowski, J.D. and C.S. Byington; "Mechanical Diagnostics Test Bed for Condition-Based Maintenance"; Presented at the Intelligent Ships Symposium II; American Society of Naval Engineers; Philadelphia; Nov 25-26, 1996.