A Real-world ap­proach to the IIoT for process re­li­a­bil­ity

Bart Win­ters, Fran­cois Le­clerc, Honey­well Process So­lu­tions

Chemical Industry Digest - - What’s In? - Bart Win­ters, Fran­cois Le­clerc

Op­er­at­ing at op­ti­mal ef­fi­ciency is im­por­tant for process plants. IIoT en­ables plant op­er­a­tors to im­prove process re­li­a­bil­ity through anal­y­sis of cap­tured data. This ar­ti­cle de­scribes how IIoT can sup­port con­tin­u­ous im­prove­ment and ad­dress pre­vi­ously un­solved prob­lems to im­prove plant avail­abil­ity, safety and re­li­a­bil­ity.

AB­STRACT

In process in­dus­tries, plants must op­er­ate not only at de­sired ca­pac­ity, but also at op­ti­mal ef­fi­ciency. This means pre­dict­ing un­de­sir­able process con­di­tions and equip­ment fail­ures be­fore they oc­cur, and then sys­tem­at­i­cally ad­dress­ing them as part of a con­tin­u­ous im­prove­ment process. The cur­rent ap­proach at many fa­cil­i­ties is to “run-to fail” since ab­nor­mal con­di­tions and mal­func­tions aren’t iden­ti­fied un­til alarms pro­vide a warn­ing or some­thing breaks.

A grow­ing num­ber of in­dus­trial fa­cil­i­ties are ex­plor­ing the power of the In­dus­trial In­ter­net of Things (IloT) to op­ti­mize their busi­ness per­for­mance. The IloT en­ables plant op­er­a­tors to im­prove process re­li­a­bil­ity by cap­tur­ing and an­a­lyz­ing data, and then iden­ti­fy­ing the warn­ing signs of po­ten­tial is­sues - pre­dict­ing when process ad­just­ment and equip­ment main­te­nance are needed, and pre­emp­tively ser­vic­ing in­stalled as­sets be­fore prob­lems arise.

This ar­ti­cle ex­am­ines the lat­est tech­niques, in­clud­ing Big Data an­a­lyt­ics, to en­able smart, con­nected plant op­er­a­tions. In par­tic­u­lar, it de­scribes how the IloT can sup­port con­tin­u­ous im­prove­ment and ad­dress pre­vi­ously un­solved prob­lems to in­crease plant avail­abil­ity, safety, and re­li­a­bil­ity. By tak­ing ad­van­tage of stream­ing data from sen­sors and de­vices to quickly as­sess cur­rent con­di­tions, rec­og­nize warn­ing signs, de­liver alerts and au­to­mat­i­cally trig­ger ac­tions, IloT-based an­a­lyt­ics so­lu­tions fun­da­men­tally trans­form pro­duc­tion and main­te­nance strate­gies.

In­tro­duc­tion

In a highly com­pet­i­tive global mar­ket­place, in­dus­trial or­ga­ni­za­tions seek “dig­i­tal in­tel­li­gence” to man­age and op­er­ate hun­dreds or thou­sands of as­sets from a sin­gle site or across an en­ter­prise to ad­dress crit­i­cal op­er­at­ing de­mands. They need ef­fec­tive tools to trans­form process data into real-time in­for­ma­tion re­gard­ing process per­for­mance, equip­ment hea lth, en­ergy con­sump­tion, and emis­sions mon­i­tor­ing.

Plant op­er­a­tors, process and equip­ment engi­neers, and man­agers re­quire con­tin­u­ous mon­i­tor­ing and sur­veil­lance, no­ti­fi­ca­tions, and col­lab­o­ra­tion with ex­perts so that ap­pro­pri­ate proac­tive ac­tions can be taken. This will min­i­mize degra­da­tion, poor per­for­mance and sec­ondary dam­age to equip­ment to re­duce costs, as well as in­crease through­put and prof­its.

In an ef­fort to en­sure up­time, com­pa­nies have his­tor­i­cally sent field tech­ni­cians out to per­form rou­tine di­ag­nos­tic in­spec­tions and pre­ven­tive main­te­nance ac­cord­ing to fixed sched­ules. This is a costly, la­bor­in­ten­sive process with lit­tle as­sur­ance that fail­ure won’t oc­cur be­tween in­spec­tions.

To im­prove ef­fi­ciency, com­pa­nies have im­ple­mented Ad­vanced Process Con­trol (APC), de­fined op­er­at­ing boundaries with their alarm sys­tem, cre­ated Key Per­for­mance In­di­ca­tors (KPls), and called upon lo­cal ex­perts to help solve op­er­at­ing prob­lems. The ef­fec­tive­ness of these mea­sures has been dif­fi­cult to sus­tain as they rely on ded­i­cated and knowl­edge­able on­site per­son­nel.

In ad­di­tion, in­dus­trial firms are look­ing for ways to make sense of vast quan­ti­ties of data that can have a sig­nif­i­cant im­pact on their per­for­mance. For in­stance, re­port­ing and in­ter­pret­ing of alarms and alerts is cen­tral to safe op­er­a­tions. It is also im­por­tant to act upon ab­nor­mal sit­u­a­tions quickly and ef­fec­tively.

To sup­port the va­ri­ety of mon­i­tor­ing and de­ci­sion sup- port ap­pli­ca­tions nec­es­sary within a man­u­fac­tur­ing fa­cil­ity, data needs to be turned into in­for­ma­tion and de­liv­ered with con­text so it can be un­der­stood and used in a myr­iad of ways by var­i­ous peo­ple.

Op­er­a­tional Ob­jec­tives

For man­u­fac­tur­ers and other op­er­at­ing com­pa­nies, as­set fail­ure and al­most im­per­cep­ti­ble re­duc­tions in process and equip­ment ef­fi­ciency are con­stant

In­dus­trial firms are look­ing for ways to make sense of vast quan­ti­ties of data that can have a sig­nif­i­cant im­pact on their per­for­mance. For in­stance, re­port­ing and in­ter­pret­ing of alarms and alerts is cen­tral to safe op­er­a­tions. It is also im­por­tant to act upon ab­nor­mal sit­u­a­tions quickly and ef­fec­tively.

To max­i­mize their over­all per­for­mance, mod­ern plants are look­ing for ways to trans­form their op­er­at­ing and main­te­nance phi­los­o­phy from “break-fix” to keep­ing op­er­a­tions run­ning as ef­fi­ciently and steadily as pos­si­ble while de­creas­ing un­planned down­tim.

threats to the op­er­at­ing plan and Over­all Equip­ment Ef­fec­tive­ness (OEE). As a re­sult, they are shift­ing their spend­ing to in­creased equip­ment main­te­nance, and thus los­ing po­ten­tial rev­enue. Fac­tors such as avail­abil­ity of skilled work­ers and in­creas­ingly com­plex pro­duc­tion pro­cesses are im­pact­ing the abil­ity to pre­dict and de­tect de­te­ri­o­rat­ing as­set health and process per­for­mance.

To max­i­mize their over­all per­for­mance, mod­ern plants are look­ing for ways to trans­form their op­er­at­ing and main­te­nance phi­los­o­phy from “break-fix” to keep­ing op­er­a­tions run­ning as ef­fi­ciently and steadily as pos­si­ble while de­creas­ing un­planned down­tim. Key op­er­a­tional ob­jec­tives in­clude:

• De­ploy on­line, con­tin­u­ous mon­i­tor­ing and ex­cep­tion based alerts for process per­for­mance, equip­ment, and con­trols

• Cap­i­tal­ize on in­creased data avail­abil­ity across the en­ter­prise

• Put data into con­text so as to com­pare as­sets to de­ter­mine sim­i­lar con­di­tions or be­hav­ior

• Im­ple­ment tools for process and re­li­a­bil­ity engi­neers en­abling vis­ual data ex­plo­ration to de­crease re­liance on com­plex ma­chine learn­ing al­go­rithms to solve prob­lems

• Es­tab­lish col­lab­o­ra­tion with both in­ter­nal and ex­ter­nal Sub­ject Mat­ter Ex­perts (SMEs). In­te­grated op­er­a­tional and main­te­nance strate­gies open up new pos­si­bil­i­ties for com­pa­nies. Data from sen­sors mon­i­tor­ing both process and ma­chine con­di­tions are com­bined to iden­tify any pat­terns that in­di­cate a pos­si­ble fault or process lim­i­ta­tion. This al­lows the on­set of a stop­page to be rec­og­nized early, and cor­rec­tive mea­sures to be planned and in­tro­duced in the most ef­fec­tive way.

Com­bin­ing both process and equip­ment data leads to truly un­der­stand­ing as­set ca­pa­bil­ity, and en­ables the def­i­ni­tion of ac­cu­rate, con­sis­tent op­er­at­ing and in- tegrity en­velopes that can be used in APC strate­gies. The re­sult is greater process sta­bil­ity within con­trol and mon­i­tor­ing sys­tems for sit­u­a­tional aware­ness at all lev­els of op­er­a­tions, as well as im­proved de­ci­sion sup­port sys­tems to en­sure as­sets are op­er­ated in an op­ti­mal man­ner. With this ap­proach, un­planned down­time can be avoided, and both staff and re­sources can be em­ployed more ef­fec­tively.

Lever­ag­ing the In­dus­trial In­ter­net

There’s no doubt the lloT car­ries ma­jor im­pli­ca­tions for in­dus­try, es­pe­cially at a time when in­fra­struc­ture is ag­ing and vet­eran op­er­a­tors and engi­neers are re­tir­ing. There is a short­age of ex­pe­ri­enced work­ers to take the place of sea­soned per­son­nel, re­sult­ing in a loss of knowl­edge. The IloT can be lever­aged to in­sti­tu­tion­al­ize knowl­edge cap­ture while re­quir­ing fewer in­ter­nal ex­perts. This can be done with the help of ex­ter­nal ex­perts, such as process li­cen­sors, who have ex­per­tise and vis­i­bil­ity beyond the com­pany’s as­sets. More­over, the IloT can have a sig­nif­i­cant im­pact on com­pet­i­tive­ness as man­u­fac­tur­ers strug­gle to pull their weight in the global eco­nomic re­cov­ery.

The IloT al­lows com­pa­nies to do more with their cur­rent sys­tems and ex­tend their busi­ness pro­cesses to en­hanc;e mon­i­tor­ing and re­duce the time to ac­tion. For ex­am­ple, a cloud-based con­trol loop and APC mon­i­tor­ing sys­tem can be set up to mon­i­tor con­trols across the en­ter­prise by an in­ter­nalor ex­ter­nal do­main ex­pert. With vis­i­bil­ity and knowl­edge across sites, ex­perts can alert and col­lab­o­rate with site SMEs and rec­om­mend ac­tions when con­trol ben­e­fit degra­da­tions are de­tected. Each site can ben­e­fit from ear­lier de­tec­tion and faster res­o­lu­tion of prob­lems af­forded by a higher level of ex­per­tise fo­cused on con­trol per­for­mance. For the en­ter­prise, these ca­pa­bil­i­ties can be de­ployed us­ing fewer re­sources than hav­ing an ex­pert at each site.

In or­der to make bet­ter busi­ness de­ci­sions, the IloT of­fers com­pa­nies the abil­ity to:

• Ag­gre­gate data from ex­ist­ing sources

• Cre­ate ad­di­tional data sources in a cost-ef­fec­tive way

• Gain vis­i­bil­ity into new data

• Iden­tify pat­terns

• De­rive in­sight through an­a­lyt­ics.

Through this ap­proach, pre­vi­ously un­solved prob­lems, as well as new ones, can be solved with as­sets com­mu­ni­cat­ing and pro­vid­ing real-time us­age data to

al­low plants to do pre­dic­tive main­te­nance and process op­ti­miza­tion.

In­dus­try-lead­ing com­pa­nies are trans­form­ing their op­er­a­tions by uti­liz­ing proven so­lu­tions in the ar­eas of process and event data col­lec­tion, com­bined process and as­set-cen­tric an­a­lyt­ics, and vi­su­al­iza­tion tech­nol­ogy to con­tin­u­ously and au­to­mat­i­cally col­lect, or­ga­nize and an­a­lyze data. In­deed, ad­vanced an­a­lyt­ics is one of the pil­lars of the lloT con­nect­ing peo­ple, pro­ces ses and as­sets to op­ti­mize busi­ness re­sults. It can trans­form work pro­cesses from man­ual and re­ac­tive to au­to­matic and proac­tive, help­ing users avoid un­planned down­time, and im­prove per­for­mance and safety.

An IIoT en­abled plant uses a com­bi­na­tion of ad­vanced sen­sors, au­to­ma­tion sys­tems, and cloud tech­nolo­gies in­te­grated with cur­rent sys­tems and data an­a­lyt­ics to be­come smarter. This pro­vides the abil­ity to lo­cate data in a cloud en­vi­ron­ment where it can be ac­cessed and an­a­lyzed with an­a­lyt­i­cal tools. For ex­am­ple, an equip­ment vi­bra­tion read­ing would be sent to the plant’s Dis­trib­uted Con­trol Sys­tem (DCS) as a sin­gle value, whereas rich dy­namic data stored in the cloud would al­low engi­neers to study the har­monic sig­na­ture of a bear­ing or shaft to de­ter­mine the root cause of a pend­ing as­set fail­ure. Cur­rently, in most cases, dy­namic data is only em­ployed by spe­cial­ists in cus­tom ap­pli­ca­tions - lim­it­ing its ac­ces­si­bil­ity by other users in the plant.

In terms of pre­dic­tive main­te­nance and process per­for­mance, IloT-based so­lu­tions en­able in­dus­trial en­ter­prises to proac­tively man­age their as­sets and make more in­formed de­ci­sions through an­a­lyt­ics at the edge. Pro­duc­tion and main­te­nance strate­gies can be com­bined for op­ti­mal over­all per­for­mance and ex­e­cuted based on how as­sets are ex­pected to func­tion to­mor­row - not solely ac­cord­ing to a spe­cific pe­ri­od­ic­ity or on par­tic­u­lar present con­di­tions.

Another key driver of the IloT is a re­duc­tion in the level of In­for­ma­tion Tech­nol­ogy (IT) skills and ex­per­tise re­quired to sup­port stand­alone ap­pli­ca­tions so that com­pa­nies can fo­cus on their core com­pe­tency of run­ning and man­ag­ing op­er­a­tions.

Mak­ing the Most of Plant Data

Ma­jor au­to­ma­tion sup­pli­ers have de­vel­oped in­no­va­tive tech­nolo­gies that de­liver real-time process and as­set-cen­tric an­a­lyt­ics, per­for­mance cal­cu­la­tions, event de­tec­tion and col­lab­o­ra­tion for plant man­age­ment, engi­neer­ing, main­te­nance, Cen­ter of Ex­cel­lence (COE) ex­perts, and op­er­a­tions. These so­lu­tions are de­signed for on­line con­tin­u­ous mon­i­tor­ing of equip­ment and process health, en­abling in­dus­trial fa­cil­i­ties to pre­dict and pre­vent as­set fail­ures and poor op­er­a­tional per­for­mance.

To­day’s tools for real-time process per­for­mance mon­i­tor­ing pro­vide sta­tis­ti­cal cal­cu­la­tions and em­bed­ded per­for­mance mod­els which, when paired with near real-time sur­veil­lance of in­stru­ments, process and equip­ment, al­low users to ac­cu­rately as­sess as­set per­for­mance. They of­fer a clear win­dow into plant pro­cesses - con­tin­u­ously mon­i­tor­ing op­er­at­ing con­di­tions, and en­abling de­ci­sions and ac­tions to pre­vent pro­duc­tion loss, min­i­mize down­time, and re­duce main­te­nance ex­penses.

The lat­est de­vel­op­ments in the field of plant equip­ment and process health mon­i­tor­ing lever­age se­cure, man­aged, and hard­ened edge-to-cloud plat­forms, while fo­cus­ing on data sci­ence and an­a­lyt­ics, and ap­ply­ing “dig­i­tal twin” pat­terns to drive their an­a­lytic mod­els. With the help of ex­ter­nal ex­perts, these so­lu­tions en­able in­dus­trial firms to ex­tract mean­ing­ful in­sights from their data. This leads to im­proved de­ci­sion mak­ing and ad­dresses such is­sues as safety im­prove­ment, as­set man­age­ment and op­ti­miza­tion of op­er­a­tions. As a re­sult, process plants are be­com­ing more agile, driv­ing in­creased rev­enue and keep­ing the fo­cus

Ad­vanced an­a­lyt­ics is one of the pil­lars of the lloT con­nect­ing peo­ple, pro­ces ses and as­sets to op­ti­mize busi­ness re­sults. It can trans­form work pro­cesses from man­ual and re­ac­tive to au­to­matic and proac­tive, help­ing users avoid un­planned down­time, and im­prove per­for­mance and safety.

on what mat­ters most - pro­duc­tion.

By mod­el­ing first prin­ci­ple com­pres­sor per­for­mance and base­line per­for­mance, for ex­am­ple, cur­rent per­for­mance can be con­tin­u­ously com­pared to de­tect both sud­den changes and long-term degra­da­tion. These events have suc­cess­fully been demon­strated to trig­ger main­te­nance ac­tiv­ity. such as chem­i­cal in­jec­tion to clear foul­ing or a com­pres­sor wash, or to ini­ti­ate fur­ther ac­tion if re­quired.

Un­like con­di­tion mon­i­tor­ing so­lu­tions fo­cused solely on equip­ment’s phys­i­cal con­di­tion, the lat­est data an­a­lyt­ics and as­set mon­i­tor­ing so­lu­tions use per­for­mance degra­da­tion as a lead­ing in­di­ca­tor of po­ten­tial­prob­lems. With the lloT, iden­ti­fi­ca­tion of per­for­mance degra­da­tion and ac­tions to be taken is con­tin­u­ously im­proved since both process and equip­ment data are used not only for a spe­cific com­pres­sor but also for all com­pres­sors of sim­i­lar de­sign and ser­vice. Some tools em­ploy pre-de­fined best prac­tice tem­plates for a wide range of equip­ment types, in­clud­ing pumps, com­pres­sors, ex­chang­ers, valves, and tur­bines. Com­bined with an in­ter­face to process de­sign sim­u­la­tion soft­ware, this so­lu­tion helps users rapidly de­ploy equip­ment or process mon­i­tor­ing on any plant as­set - elim­i­nat­ing the need for com­plex model de­vel­op­ment.

It is im­por­tant to re­mem­ber that the IloT is not just about cap­tur­ing sen­sor data. In­for­ma­tion needs to be put into the as­set con­text struc­ture; merely op­er­at­ing on tag-based data will not en­sure a re­peat­able and scal­able so­lu­tion. Pro­cesses are in­stru­mented for con­trol rather than re­li­a­bil­ity or op­ti­miza­tion, and as a re­sult, much of the “de­rived data” im­por­tant for pre­dic­tion and de­ci­sion-mak­ing is locked in spread­sheets and other stand­alone tools. It is es­sen­tial to con­tin­u­ously cal­cu­late this data and bring it into the IloT en­vi­ron­ment where con­tin­u­ous run­time an­a­lyt­ics can exam-

ine his­tor­i­cal per­for­mance for use in ma­chine learn­ing al­go­rithms.

Fur­ther­more, IloT so­lu­tions should not solely rely on a sta­tis­ti­cal model to de­tect de­vi­a­tions from nor­mal. Hav­ing a fun­da­men­tal, physics-based model cre­ates a dig­i­tal twin, with a vir­tual rep­re­sen­ta­tion of the process or as­set lo­cated in the cloud. This al­lows users to model and com­pare ex­pected process per­for­mance against ac­tual re­sults, and then ap­ply these de­vi­a­tions as early in­di­ca­tors of health degra­da­tion.

Dig­i­tal twins ex­ist at the in­ter­sec­tion of phys­i­cal engi­neer­ing and data sci­ence, and their value trans­lates di­rectly to mea­sur­able busi­ness out­comes: re­duced as­set down­time, lower main­te­nance costs, im­proved plant and fac­tory ef­fi­ciency, re­duced cy­cle times, and in­creased pro­duc­tiv­ity.

Ben­e­fits to Indu-strial Or­ga­ni­za­tions

Rapid adop­tion of the IIoT has cre­ated economies of scale for smart sen­sors, con­nec­tiv­ity, an­a­lyt­ics, and ro­bust soft­ware plat­forms. This change is driv­ing the adop­tion of en­ter­prise-level per­for­mance man­age­ment, process mon­i­tor­ing, pre­dic­tive main­te­nance pro­grams, and busi­ness trans­for­ma­tion with the goal of elim­i­nat­ing un­planned down­time and re­duc­ing op­er­at­ing costs, while main­tain­ing prod­uct qual­ity and com­pli­ance.

A real world ap­proach to the IIoT en­ables the in­te­gra­tion of cur­rent sys­tems and the ad­di­tion of new data sources and an­a­lyt­ics to sup­port com­ple­men­tary, con­tin­u­ous im­prove­ment pro­cesses fo­cused on per­for­mance mon­i­tor­ing and de­ci­sion sup­port. The spe­cific ben­e­fits of this ap­proach in­clude:

• In­crease process re­li­a­bil­ity and as­set uti­liza­tion up to 10% - Plants can re­duce un­planned down­time by defin­ing and op­er­at­ing within op­er­at­ing and in­tegrity en­velopes, pre­dict­ing fail­ures and pro­vid­ing proac­tive re­sponses, as well as min­i­miz­ing rate and ef­fi­ciency losses.

• In­crease op­er­at­ing ef­fi­ciency up to 10% - In­dus­trial or­ga­ni­za­tions can man­age per­for­mance, in­clud­ing yields, en­ergy and raw ma­te­rial us­age, to achieve up to 10% re­duc­tion in costs. This re­sults from en­hanced engi­neer­ing and pro­duc­tion ef­fec­tive­ness with con­tin­u­ous mon­i­tor­ing, re­mote col­lab­o­ra­tion and ready ac­cess to re­quired in­for­ma­tion, as well as im­proved de­ci­sion sup­port.

• Sus­tain ad­vanced con­trol and pre­ventable degra­da­tion with ben­e­fits up to 25% - Con­trol teams can proac­tively main­tain the ef­fec­tive­ness of con­trol loops, con­trollers and mod­els; ad­just con­trols to new op­er­at­ing con­di­tions and process changes; and quickly ad­dress crit­i­cal in­stru­ment is­sues.

• In­crease safety - Pro­duc­tion fa­cil­i­ties can min­i­mize risks by en­sur­ing nor­mal and sta­ble op­er­a­tions, and also elim­i­nate pro­duc­tion stops for safety sys­tem ver­i­fi­ca­tion.

• Re­duce main­te­nance costs up to 10% - Op­er­a­tions teams can take proac­tive mea­sures to min­i­mize equip­ment dam­age and emer­gen­cies while op­ti­miz­ing main­te­nance based on real as­set con­di­tions, thus im­prov­ing re­li­a­bil­ity and ex­tend­ing equip­ment life.

Con­clu­sion

Or­ga­ni­za­tions across the process in­dus­tries are seek­ing to im­prove their re­turn on large as­set in­vest­ments. Ef­fec­tively man­ag­ing as­sets, how­ever, re­quires a wealth of in­for­ma­tion and anal­y­sis. In­dus­trial fa­cil­i­ties need com­bined pro­duc­tion and main­te­nance strate­gies to min­i­mize un­sched­uled shut­downs and op­ti­mize prod­uct qual­ity while cost-ef­fec­tively us­ing the op­er­a­tions, main­te­nance and engi­neer­ing re­sources they have on hand.

The true value of the IIoT can only be fully re­al­ized with a holis­tic view of as­set man­age­ment. Pow­er­ful vir­tual cloud net­works will con­tin­u­ally col­lect, ag­gre­gate and model data for ac­cu­rate pre­dic­tion of degra­da­tion and fail­ures, and put con­tin­gen­cies in place to limit their im­pact on sys­tem avail­abil­ity. This ap­proach is be­com­ing fun­da­men­tal to im­prov­ing process re­li­a­bil­ity and driv­ing cost take­out by de­liv­er­ing real-time, in­tel­li­gent and ac­tion­able data to con­nected sys­tems and the end user. Although it may take time for some com­pa­nies to be­come an IloT data-driven or­ga­ni­za­tion, this evo­lu­tion is com­ing and they should be­gin pre­par­ing for it.

Rapid adop­tion of the IIoT has cre­ated economies of scale for smart sen­sors, con­nec­tiv­ity, an­a­lyt­ics, and ro­bust soft­ware plat­forms. This change is driv­ing the adop­tion of en­ter­prise-level per­for­mance man­age­ment, process mon­i­tor­ing, pre­dic­tive main­te­nance pro­grams, and busi­ness trans­for­ma­tion with the goal of elim­i­nat­ing un­planned down­time and re­duc­ing op­er­at­ing costs while main­tain­ing prod­uct qual­ity and com­pli­ance.

Many in­dus­trial or­ga­ni­za­tions view the IIoT as key to re­duced down­time, lower op­er­at­ing costs, im­proved reg­u­la­tory com­pli­ance, and bet­ter over­all prod­uct qual­ity

Real-time process per­for­mance rnon­i­tor­ing pro­vides an ex­panded view of op­er­a­tions to help plant per­son­nel main­tain the health of crit­i­cal as­sets.

Equip­ment fail­ures have a sig­nif­i­cant im­pact on in­dus­trial op­er­a­tions, mak­ing it im­per­a­tive to op­ti­mize pre­dictve main­te­nance strate­gies

To­day’s process plants are faced with meet­ing ever-in­creas­ing per­for­mance de­mands, which starts with im­proved ef­fi­ciency and re­duced down­time.

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