Predic've and prescrip've maintenance

Transcription

1 Predic've and prescrip've maintenance Michela Milano Dipar&mento di Informa&ca Scienza e Ingegneria Bologna 12th April 2017

2 Mainteinance is EXPENSIVE!! Maintenance costs amount more than one-third of the total operating expenses. Typical Facility Operating Cost Breakdown Failure Costs: 35% 27% 38% Unplanned downtime The cost of secondary equipment damaged by the initial failure Inefficient use of the staff resources Unneeded maintenance. Maintenance Janitorial Utilities Source: IFMA, October 2009 Despite the importance and expense of maintaining building efficiency most building operators rely on reactive maintenance programs to care for their equipment.

3 Mainteinance levels Maintenance is a core asset at industrial level Downtime reduction Production loss reduction Breakdown reduction Three maintenance levels so far Reactive maintenance: a fault is perceived and it is repaired Preventative: regular maintenance on a machinary to reduce fault probability Predictive: real fault prediction on the basis of sensor values Beyond predictive: PRESCRIPTIVE MAINTENANCE DATA FROM MACHINARIES ARE ESSENTIAL

4 Fault prediction Data streams coming from sensors 120 sensors sampled every 30 seconds Time stamp Machine ID Sensor ID Sensor value Sensor Sensor Sensor n 6482 Sensor Sensor 2 Data preprocessing Divided per machine average/variance per minute/hour

5 Fault prediction Look-back period Step size Time series data stream event event Survey SAP on predictive maintainance Predicts: Time to fault/probability of fault Which sensors are representative

6 Input: Fault prediction: traditional approach Time series of sensor values in a observation window 168 inputs (mean/variance on one hour) for a week There are ML methods that enable to identify the most representative values Decision trees/random forests Attribute values: sensors and sensor values Class: fault/no-fault Decision trees identify the most informative sensors Sometimes low accuracy Deep networks can help: stacked denoising autoencoders

7 Auto-encoders Auto-encoders are neural networks that learn a representation of the input and is able to reconstruct it [Figure by R. Salakhutdinov] Unsupervised learning Minimise reconstruction error

8 Auto-encoders Number of hidden units lower than input features Feature selection Dimensionality reduction Number of hidden units larger than input features Parameter induced sparsity Discover non-linear relations in data that are not easily captured by statistical methods Denoising auto-encoders: input corruption to reduce overfitting Stacked denoising auto-encoders: more autoencoders in a stack

9 Fault prediction How to use auto-encoders? They take the original sequence as input and compress it Mean/variance on one minute: input Output neurons: 200 (feature selection) Two hidden layers of 1000 neurons each

10 Fault prediction How to use auto-encoders? They take the original sequence as input and compress it On the compression we use a ML method (DT or RF) DECISION TREE FEATURES EXTRACTED FROM SDA DENOISING AUTOENCODER RAW DATA FROM SENSORS Compared to ML method using average and variance on a time windows 10-20% Accuracy improvement

11 Prescriptive maintenance Beside predictions, we need an optimization component being able to exploit predictions to OPTIMALLY PLAN MAINTENANCE INTERVENTION DEFINE OPTIMAL PRODUCTION/MAINTENANCE PLANS DEFINE MAINTAINANCE CREWS SHIFTS Predictions are black boxes DEFINE OPTIMAL OPERATIONS CONDITIONS to DECREASE FAULT PROBABILITIES Real integration predictive prescriptive analytics

12 SOLVING COMPONENT OUTPUT MAINTAINANCE PLANS MAINTAINANCE PLANS VISUALIZATION Stochastic Multi-Criteria Optimization Model BDSS OBJECTIVE FUNCTIONS Maintenance costs Breakdowns Downtime Production Model Integration MARKET MODEL Demand profile Cost of energy FAILURE MODEL For plant/component Temporal model for failure prediction with confidence levels COST MODEL For plant/component Function that links intervention plan with corresponding cost PRODUCTION MODEL For plant/component Function that links intervention plan with production losses Machine Learning Machine Learning Machine Learning PLANT DATA Static data on plant config. Asset Mgt TEMPORAL SERIES Production data per component/ plant DATA LAYER HISTORICAL DATA MAINTAINANCE Processes/costs/ Time/loss MARKET DATA Demand/ Energy cost Predictions

13 Prescriptive maintenance techniques Combinatorial optimization techniques are core to prescriptive tools. Efficient exploration and pruning of huge search spaces Modelling and solving techniques: Deterministic/stochastic models Stochastic models take into account uncertainty of predictions Single criteria/multi-criteria optimization algorithms Heuristics/exact methods depending on the scale and the time we have at disposal ANYTIME ALGORITHMS Hybrid pruning based on feasibility and optimality

14 Prescriptive maintenance plus Case study: energy production (oil-gas) Breakdown reduction : 70%-75% Downtime reduction : 35% - 45% Maintenance cost reduction: 25%- 30% Production increase: 20%- 25% Roland Berger Strategy Consultants, Survey 2014

15 Make IT Simple Synapse Maintenance Suite Smart failure detection. Predict the likelihood of failure. Optimal production and maintenance plan

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19 Lesson learned Predictive and prescriptive analytics can really help reducing maintenance-related costs. Need of good quality data Need of a high volume of data Possibly we need human intervention in tagging data for the classification

20 Predic've and prescrip've maintenance Michela Milano Dipar&mento di Informa&ca Scienza e Ingegneria Bologna 12th April 2017