has your
plant failed again?
Take intel­li­gent pre­ven­tion now!

Do you want to avoid plant fail­ures and machine down­times at all costs? Do you want to per­form intel­li­gent and con­tin­u­ous con­di­tion mon­i­tor­ing and pre­dic­tive main­te­nance of your machines and plants? Do you want to imple­ment effi­cient and proac­tive main­te­nance of your machin­ery and equip­ment using arti­fi­cial intel­li­gence instead of rely­ing on tra­di­tion­al main­te­nance sys­tems? Then you are obvi­ous­ly in need of a smart pre­dic­tive main­te­nance solu­tion that meets the require­ments of Indus­try 4.0. Won­der­ing what you need for this? That’s easy: your col­lect­ed data and our soft­ware solu­tion s.maintenance.

The chal­lenge

Smart predictive maintenance
of machines and plants

Qual­i­ty defects, pro­duc­tion faults, unplanned machine down­times and plant fail­ures are the great­est cost dri­vers in indus­try. The most com­mon cause: Wear com­po­nents such as valves, fil­ters or fans are replaced too late or not at all. All of these prob­lems can be avoid­ed through proac­tive mea­sures and pre­dic­tive main­te­nance. Pre­dic­tive main­te­nance tools deter­mine the best point in time for main­te­nance and oth­er tech­ni­cal ser­vices and issue ear­ly warn­ings. This enables you to react quick­ly and in a well-planned fash­ion to immi­nent fail­ures, and thus min­imise down­time.

It all begins with your digital data!

The more reli­able you want the pre­dic­tions about the con­di­tion of your machines and equip­ment to be, the more mean­ing­ful and com­pre­hen­sive the data needs to be that is fed into the smart pre­dic­tive main­te­nance tool. This data is processed and analysed by intel­li­gent algo­rithms. You will quick­ly see how the mon­i­tor­ing tool can help you devel­op a proac­tive main­te­nance strat­e­gy and ensure trou­ble-free oper­a­tion through machine learn­ing. With this knowl­edge, you can plan your projects and process­es much more effec­tive­ly with­out hav­ing to wor­ry about unex­pect­ed fail­ures of machin­ery or equip­ment. Our fore­cast­ing soft­ware will not only spare your nerves, but also help you cut costs.

Spurensucherin steht vor Baumstamm mit neuronalem Netzwerk.

our software solution s.maintenance

Thanks to our data-dri­ven soft­ware s.maintenance, we can not only make pre­cise pre­dic­tions about the remain­ing ser­vice life of wear com­po­nents, but also max­imise it and ini­ti­ate main­te­nance activ­i­ties ear­ly. Thus, s.maintenance helps achieve a more effi­cient use of resources and cut main­te­nance costs. Our pre­dic­tive main­te­nance soft­ware takes into account the actu­al wear and pre­dicts com­po­nent wear before any machine fail­ure can occur. This allows you to keep a per­ma­nent eye on the cur­rent func­tion­al state of your plant or machine, con­tributes to a more effec­tive spare parts man­age­ment and sig­nif­i­cant­ly reduces and opti­mis­es your service/maintenance inter­vals and activ­i­ties.

Monitor im Wald mit Diagramm

the algorithms of s.maintenance

The wear and tear of parts main­ly depends on fac­tors such as process para­me­ters and inten­si­ty of use. Apply­ing a machine learn­ing approach, the algo­rithm of s.maintenance uses this infor­ma­tion to cre­ate a pre­cise mod­el describ­ing the wear-out dynam­ics of a spe­cif­ic com­po­nent. On this basis, it can make pre­cise pre­dic­tions as to when crit­i­cal usage or fail­ure lim­its are reached. The result is a con­tin­u­ous, intel­li­gent and auto­mat­ic mon­i­tor­ing sys­tem for rel­e­vant wear com­po­nents that is used to plan and cre­ate a pre­dic­tive main­te­nance sched­ule for the machine or an entire pro­duc­tion line.

At a glance

THE BENEFITS OF s.maintenance

1 ensures effi­cient use of wear com­po­nents
2 min­imis­es high rework­ing costs and rejects, and thus, qual­i­ty defects in man­u­fac­tured prod­ucts
3 pre­dictable spare parts pro­cure­ment min­imis­es sup­ply chain risks
4 ensures that resources are used spar­ing­ly and reduces main­te­nance spend­ing
5 pre­vents unplanned dis­rup­tions or fail­ure of equip­ment
6 increas­es plant safe­ty and ser­vice life
7 increas­es over­all equip­ment effec­tive­ness (OEE)
8 enables long-term opti­mi­sa­tion of pro­duc­tion process­es
Your con­tact to ifm stat­math
Do you have ques­tions about our prod­ucts? Con­tact us to book an appoint­ment. Our team will be hap­py to advise you per­son­al­ly or via email.
statmath Mitarbeiter Lukas
How it works

s.maintenance IN A NUTSHELL


  • his­tor­i­cal time series data of sen­sors

  • wear com­po­nents replace­ment times

  • oth­er main­te­nance data from his­tor­i­cal main­te­nance sched­ules



  •  iden­ti­fy pat­terns and cor­re­la­tions in the behav­iour of rel­e­vant wear com­po­nents, such as fil­ters, fans, spray valves, etc.
  • under­stand indi­vid­ual wear-out dynam­ics and cre­ate a pre­dic­tive main­te­nance sched­ule or main­te­nance strat­e­gy for entire pro­duc­tion lines
  • pre­cise pre­dic­tion as to when unde­sired oper­at­ing states such as usage or fail­ure lim­its of mon­i­tored wear parts are reached to make best use of ser­vice life


  • pre­dic­tive main­te­nance of your machin­ery and plants
  • avoid­ance of down­time
  • effi­cient use of resources
  • effec­tive reduc­tion of main­te­nance costs
  • user-defined pro­gram­ming inter­face (API) for planned deriva­tions
Alexander Hoffmann
“Today, pre­dic­tive main­te­nance is THE require­ment of Indus­try 4.0. s.maintenance is our solu­tion for pre­dict­ing the remain­ing ser­vice life of wear parts in machines and instal­la­tions intel­li­gent­ly and pre­cise­ly. This pre­serves resources, saves costs and max­imis­es effi­cien­cy.”
ifm stat­math Man­ag­ing Direc­tor
Dr. Alexan­der Hoff­mann