IBM Maximo Asset Management MAHI - Maximo Asset Health Insights

Präsentation zum Thema: "IBM Maximo Asset Management MAHI - Maximo Asset Health Insights"—  Präsentation transkript:

1 IBM Maximo Asset Management MAHI - Maximo Asset Health Insights
Carsten Frentz-Bernt Technical Sales IBM Watson Internet of Things Ulm, Germany 17. Mai 2017

2 Please Note IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.

3 Overview - Vorbeugende Wartungsprogramme verbessern die Zuverlässigkeit ...
Viele Unternehmen haben mehr WI-Pläne als Mitarbeiter, um diese auszuführen Optimierung und Priorisierung von WI-Pläne wird notwendig Aufwand für WI-Pläne ist dem Anlagenzustand anzupassen 40% der vorbeugenden Instandhaltungskosten verteilen sich auf Vermögenswerte mit vernachlässigbarem Einfluss auf die Maschinenlaufzeit / -verfügbarkeit 1 Für traditionelle, vorbeugenden Wartungsprogramme wird ein zeitbasierter Ansatz angewendet ... ... daraus resultierende Probleme : Verbrauch teurer Ressourcen Potenzielles Einbringen von Fehlern durch Störung stabiler Systeme 30% der vorbeugenden Instandhaltungsmaßnahmen werden zu häufig durchgeführt 2 45% aller Wartungsarbeiten sind unwirksam 2 Many of our Maximo customers have effectively used Maximo to implement preventive maintenance programs which have helped improve reliability. However, in most cases they are not as efficient as they could be. Many organizations have relied on calendar based preventive maintenance, and while these do help improve reliability they also result in cases where an asset may fail prior to it’s scheduled maintenance, or cases where maintenance is done earlier than it actually needs to be. Many organizations have built up a PM program with a considerable backlog of maintenance which they have trouble executing with the staff they have on hand resulting in defferals of maintenance or costly overtime to address. With the help of IoT we can gain insight into the health of the assets and use that knowledge to make key decisions that will catch problems before they occur or allow maintenance for healthy assets to be deferred. 1 Source: T.A. Cook, Maintenance Efficiency Report 2013, August 2 Source: Oniqua Enterprise Analytics, Reducing the Cost of Preventative Maintenance,

4 die Ergebnisse sprechen für sich ...
Overview - Zustandsbasierte Instandhaltung nutzt IoT-Daten zur Beurteilung des Anlagenzustands Überwachen und Analysieren des Anlagenzustands basiernd auf historischen und Echtzeit-Daten Zum richtigen Zeitpunkt eingreifen, noch vor dem Anlagenstillstand Optimierung und Priorisierung von Aufwänden für Ressourcen 40% of preventive maintenance costs are spent on assets with negligible affect on uptime 1 Reduzierung der Instandhaltungskosten um bis zu 25% Vermeiden von bis 70% der Anlagenstillstände Reduzierung der Ausfallzeiten um bis zu 50% die Ergebnisse sprechen für sich ... ungeplante Ausfälle um bis zu 50% senken geplanten Reparaturen um bis zu 12% reduzieren 3-5% der Kapitalaufwände einsparen There have been a number of studies showing the business and financial benefits of leveraging information from the equipment to drive maintenance effectiveness and you can see some of the substantial savings listed here. Reduce maintenance costs by up to 25% - Source: Fortune Cut unplanned outages by up to 50% - Source: Fortune Reduce downtime by up to 50% - Source: McKinsey (page 80) Reduce scheduled repairs by up to 12% - Source: Accenture (pg 4) Eliminate up to 70% of breakdowns - Source: Accenture (pg 4) Reduce capital investment by 5% - Source: McKinsey (page 80)

5 Overview - Maximo Condition Monitoring / Zustandsüberwachung ... heute
Continuous Continuous Gauge Characteristic Zähler Kilometerzähler fortlaufendes Zählen Messgeräte Thermometer Messwert schwankt / pendelt Beobachtungen Zustand diskrete Werte WI-Plan Continuous Grenzwertüberwachung Einige Beispiele : wenn der Füllstand des Tanks niedrig ist, initiiere die Arbeit zum Wiederauffüllen wenn die Druckdifferenz an einem Filter über Wert x liegt, ersetze den Filter wenn der Ausgangsdruck für eine Pumpe sinkt, plane eine Inspektion / Wartung Messwerte, die benutzerdefinierte Werte überschreiten, lösen einen WI-Arbeitsauftrag aus We have had in Maximo for the last few years a very simple way to do condition monitoring. There is a capability in core Maximo to define meters that are associated with an asset. These meters can be of three types that you see here. Those that aggregate over time, those that fluctuate up and down, and characteristic meters typically based on values from inspections. These meters support some very simple use cases, but many clients have been able to find value from them, and here are some examples of use cases. Based on the value of the meters, you can define warning levels or action levels which will automatically generate a work order with the appropriate job plan.

6 Overview - nur den aktuellen Anlagenzustand zu kennen, ist nicht genug ...
Asset Health Sensordaten Echtzeit & Historie Maximo Anlagen- information Wetter Daten Zustandsinformationen werden wertvoller, wenn sie mit kontextuellen Informationen kombiniert werden, um ein perfektes Bild zum Anlagenzustand zu geben Echtzeit-Sensordaten aus der Anlage / dem Gerät über einzelne Sensoren Echtzeit-Sensordaten oder Warnungen aus SCADA-Systemen Historische Trends der Sensordaten aus anderen Data Repositories Inspektionsdaten, die Messungen, Beobachtungen, Fotos, etc umfassen und manuell oder über mobile Endgeräte gesammelt wurden Historie der Instandhaltungsaktivitäten zur Anlage zugehörige Informationen : Umwelt (Temp, Feuchtigkeit, etc.), Produktion (Nutzung & Leistung), Wetter ... But really, many of our clients are asking for capability beyond a single data value at one point in time. They are looking for greater insight that could take into account multiple sensor values, trends of the data over time, as well as the rich set of information about the asset such as it’s age, it’s maintenance and failure history and current open work order status. Also in many cases, the historical weather information related to the location where the asset is operating is an important indicator of asset health.

7 Overview - IBM Maximo Asset Health Insights
Entwickelt für Betriebsingeneure und Anlagenbetreiber Unterstützt Warnhinweise und Benachrichtigungen vor dem Anlagenausfall Anzulagenzustand beurteilen auf Basis von Daten aus verschiedenen Quellen, einschließlich Maximo und Echtzeit - Daten : Verbleibende Nutzungsdauer Wartungs- und Fehlerhistorie Zustand basierend auf Echtzeit und historischen Informationen Nutzung von historischen Wetterdaten als Schlüsselelement bei der Bestimmung des Anlagenzustands Reduzierung von unnötigen Instandhaltungsaktivitäten durch berechnete Zustandsfaktoren zur Anlage And so, we have recently released a new Maximo product called IBM Maximo Asset Health Insights. It is designed for the reliability engineer in mind, and brings all of the information needed by the engineer into one place. It is fully integrated with the Watson IoT Platform and analytics capabilities, and it leverages the new UI/UX technology available for Maximo. They can define warnings and notifications which can be leveraged to take actions prior to a potential failure. Asset Health also enables the reliability engineer from right within Maximo to leverage their knowledge to define the asset health calculation by asset class which can include a rich combination of sensor data and/or any other object within the asset record. It allows them various drill down views into the data, and provides context based actions for them to achieve optimal results. This gives them insights into key decisions around PM optimization as well as when to replace an asset.

8 Overview - Benutzeroberfläche
Einheitliche Optik und User Interface in Maximo 7.6 This is a screen capture of what the application looks like. It is using the new UI/UX look and feel introduced with Maximo 7.6, and provides the reliability engineer with capabilities to bring all of the information together, define and analyze asset health scores, and take appropriate actions.

9 Overview - Kontextbezogene Informationen zu Anlage
Meter Readings berechnete Zustandsfaktoren zur Anlage Echtzeit Sensordaten You are also able to drill into specific assets to understand the elements of the health score, historical weather information, or trends in the IoT data coming from the equipment. historische Wetterdaten

10 Formeln - Scoring System

11 Formeln - Exemplarische Formeln für die ersten Schritte
Driver Variation Formula Remaining Useful Life (%) Remaining Useful Life (% of Expected Life) PCT((NVL(EXPECTEDLIFE,10) - MSINCE(NVL(installdate,$sysdate$))/12),NVL(EXPECTEDLIFE, 10)) Remaining Useful Life () Remaining Useful Life (Years) (NVL(EXPECTEDLIFE,10) - MSINCE(NVL(installdate,$sysdate$)) Remaining Useful Life (based on run hours) (NVL(mfgrunlife$numvalue,10) - runhours$numvalue) /mfgrunlife$numvalue Remaining Useful Life (financial life) IF(totalcost>replacecost,0,100-PCT(totalcost,replacecost)) Remaining Useful Life (using asset status history) (NVL(mfglife$numvalue,10) - MSINCE(NVL(oldest$assetstatus$changedate,statusdate))/12)/mfglife$numvalue Condition  Based on Temperature (Temp-F in demo database) IF(meterval("Temp-F")>100 || meterval("Temp-F")<40,0,1) Observation Based on OilColor meter (characteristic) Imeterval("OilColor") Performance Mean Time Between Failure (MTBF) (NVL(mfgrunhours$numvalue,10) - runhours$numvalue) /<$numvalue Currently Running isrunning OEE OEE = (Good Count × Ideal Cycle Time) / Planned Production Time) MTTR Downtime/Total Times Down Cost Cost (YTD spend vs Budget) ASSET.YTDCOST / ASSET.BUDGETCOST Cost (LTD spend vs Replacement Cost) Work Open Work Orders IF(count$openwo>0, 0, 1) Weather Avg Temp for a Period of TIme WHAVG("temp",10)  Maximum Temp for a number of days WHMAX("temp",10) Minimum temp for a number of days WHMIN("temp",10) Current temp WCVAL("temp") Number of times the temp exceeds a value over a number of days WHTCOUNT("temp", 10, 100, 1) Link zur Formelsammlung Dokument - Erstellen von Formel in Maximo

12 Connecting - IBM Maximo Asset Health Insights - Datenquellen
1 Echtzeitdaten aus einfacher Sensorik 4 Wetter Daten IBM Maximo Asset Health Insights Kontext Monitoring Filter Speicherung 2 gefilterte Echtzeitdaten aus Automatisierung 3 5 Historische Daten PMQ MAHI is fully integrated with the Watson IoT Platform which is leveraged to bring sensor information about the equipment into Maximo. There are several ways this can be done. First through new add on sensors which are becoming increasingly inexpensive from $20 and up. Many of the new sensor technologies are aggregated into a local gateway and from there can provide data to the Watson IoT Platform. Secondly, existing systems such as SCADA or BMS systems are collecting relevant information about the assets, and we can bring that data in via the SCHAD AMR system previously described, or other gateways that may be on the market. Thirdly, if you are already collecting data and storing in a historian such as OSI PI or other, that data can also be brought into the IoT Platform. Once there, we associate the data with assets in Maximo, and use streaming analytics to detect anomalies, and provide storage and additional filtering as needed. MAHI can then leverage this data along with data in Maximo to provide a full picture of the health of the asset. While we do allow automated generation of work orders, most clients have asked for visibility and insight into the health, so they can determine the proper course of action.

13 Connecting - IBM IoT Platform Integration mit Automatisierungssystemen
IBM Partnerschaft mit Schad Automation SCHAD Automatic Meter Reading (AMR) Verringert die Notwendigkeit von Vorort-Inspektionen und manuellen Zählerablesen Software Produkt zu Anbindung von Steuerungssystemen (SCADA, SPS, GLS) über eine breite Palette von Industrie-Standard-Protokollen wie Simatic, OPC, BACnet etc. Unterstützt das Mapping von Sensoren und Anlagen in Maximo Ermöglicht Daten aus dem Steuerungssystem an Maximo zu senden, und somit Unterstützung der vorhandenen Maximo Condition Monitoring-Fähigkeiten MQTT Integration zu IBM MessageSight für eine effiziente und sichere Remote-Standortvernetzung IBM IoT Foundation zur Bereitstellung von Daten Die Fähigkeit, einfache Regeln für Benachrichtigungen oder für die Erstellung von Arbeitsaufträgen / Serviceanforderungen in Maximo definieren Schnelle Anbindung von SPS & SCADA Informationen in Maximo Daten können für Trendanalysen zur Optimierung der vorbeugenden Instandhaltung genutzt werden AMR ist hoch skalierbar, und in der Lage Millionen von Datenpunkte über versch. Standorte in Maximo einzulesen One of the difficulties our clients have had in using condition monitoring is that there is no out of the box solution for integrating with other systems to put the data in the asset meters within Maximo. This is one of the reasons why IBM has formed a partnership with a company called SCHAD. They have considerable experience working with automation systems, and they have a product called Automatic Meter Reading which IBM is providing to fill the gap in this space. It has out of the box integration with a wide variety of control systems such as SCADA, PLCs, BMS systems. It can filter this data and provide to Maximo through the standard Maximo Integration Framework, and in addition it can provide the data to the Watson IoT Plaform for further analytics.

14 Maximo Asset Health Insights
Asset Health Themen für 2017 Q2 Release Anlagenzustand bewerten Wartungspläne optimieren Riskien reduzieren Tausch oder Reparatur vergleichen Techniker können "Faktoren" (Formeln) in einer Bibliothek definieren und diese als Info für Entscheidungen anwenden. Dies ermöglicht zusätzliche Einblicke, indem Messungen und Beobachtungen zusammengefasst und Faktoren zur Anlage berechnet werden. Unterstützung durch MAHI, um einen vorbeugenden Wartungsplan (PM) zu optimieren, indem sie entweder Daten des Wartungsplan von Maximo nutzen oder die Fehlerhistorie und IoT-Ereignisdaten an PM on Cloud (PMQ) zur Optimierung übergeben. Dem Techniker wird eine Sicht auf die Gesundheits-, Sicherheits- und Umwelt-Untersuchungen (HSE Investigations) am Arbeitsplatz zur Verfügung gestellt, sofern eine HSE-Lizenz existiert. Unterstützung für kaufmännsiche und / oder technische Entscheidung über die Reparatur oder den Ersatz von Anlagenkomponenten durch eine Kombination von verfügbaren / berechneten Daten. Integration mit Integration mit PMQ HSE (FMEA/FMECA) (FMEA) failure mode and effects analysis (FMECA) failure modes, effects and criticality analysis.

15 Anlagen mit Maximo Asset Health Insights besser verstehen
Ausfallzeiten und Wartungskosten reduzieren durch optimale Anlagenzustand Ermöglicht Ingenieuren und Technikern ein tieferes Verständnis über den Zustand ihrer technischen Anlagen zu erlangen. Die Lösung bietet die Möglichkeit, die Darstellung der Anlagenzustände zu modellieren, zuzuordnen, zu überwachen und zu optimieren. Visualisierung des Anlagenzustand über den Lebenszyklus hinweg Konsolidiert Anlagendaten, historische und Echtzeit Werte, einschließlich Daten aus externen Datensystemen, Wetterdaten etc. Die erweiterte Dashboard-Ansicht ermöglicht proaktive Handeln in Bezug auf Anlagen und IH Aktivitäten Ermöglicht Sofortmaßnahmen und / oder bietet Technikern den direkten Zugriff auf Sensor und Anlagen-Daten, um die richtigen Massnahmen einzuleiten

16 Backup

17 Summary Maximo Asset Health Insights can help optimize your PM program today by connecting your assets New formula capability provides flexibility to assess asset health based on any IoT or Maximo objects IBM is continuing to extend Maximo Asset Health Insights to help improve asset performance 9/21/2018

18 Maximo Asset Health Insights - Roadmap
Future Reliability Engineer Risk and Criticality Leverage Watson Analytics (cognitive) 2D Schematic integration Next best possible action Maintenance Supervisor Asset health in the field 2017 MAHI x 10/16 MAHI Reliability Engineer Preventive Maintenance Optimization Dashboard Integration to PM on Cloud (PMQ) Risk reduction through failure analysis (HSE Integration) Compare Refurbish vs. Replace 7/16 Reliability Engineer Support for locations Asset health history MAHI 7.6 Reliability Engineer Map Sensor data to assets Define Filtering rules in IoT Platform Analytics Define Asset Health scoring methods Display asset health on map or in list Enable key actions (Open WO, Schedule PM, Notify, etc) * Deliverables subject to change at any time based on customer feedback. Saas and Saas Flex Installation & Monthly Pricing

19 What does a deal look like?
Small – Water Utility Example – 400 pumps Maximo Asset Health Insights 400 assets, using RVU calculator is 40 RVUs x $ xxx per RVU = $ xxx list Watson IoT Platform & Bluemix services Estimate using IoT DET, 1 message a minute 24 hours a day for message that is 0.38 MB per message (or 400 messages of size MB combined) plus 100% RTI = $xxx per month List Price Weather – access for Asset Health customers $ xxx one time charge, $ xxx /month subscription SCHAD Automatic Meter Reading 2 data points per asset x 400 assets, 800 data points = $ xxx list Total Deal Price at List Prices - $ XXX one time charge and $ XXX /month subscription